WO2023138558A1 - 一种图像场景分割方法、装置、设备及存储介质 - Google Patents

一种图像场景分割方法、装置、设备及存储介质 Download PDF

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WO2023138558A1
WO2023138558A1 PCT/CN2023/072537 CN2023072537W WO2023138558A1 WO 2023138558 A1 WO2023138558 A1 WO 2023138558A1 CN 2023072537 W CN2023072537 W CN 2023072537W WO 2023138558 A1 WO2023138558 A1 WO 2023138558A1
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segmentation
scene
layer
initial
map
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French (fr)
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朱意星
黄佳斌
王一同
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Beijing Zitiao Network Technology Co Ltd
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Beijing Zitiao Network Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging

Definitions

  • Embodiments of the present disclosure relate to the technical field of image processing, for example, to an image scene segmentation method, device, device, and storage medium.
  • Image scene segmentation as one of the processing research directions of image processing, is mainly used to separate the scenes included in the image according to the scene category.
  • scene and object segmentation based on deep learning technology has achieved relatively large breakthroughs in recent years.
  • the deep learning network used for image scene segmentation in the related art has obvious effects in the segmentation of single-category or few-category scenes and objects, and the technology is relatively mature.
  • accurate segmentation cannot be achieved for images with multiple categories of scenes, often resulting in fragmented segmentation results. If the segmentation results are directly applied to downstream business implementation, it will affect the execution effect of downstream business.
  • the improvement methods in the related technologies mainly consider directly optimizing the deep learning network to optimize the scene segmentation results.
  • the deep learning network is overly dependent on the training data set. Due to the ambiguity between many scene categories, it is impossible to provide accurate sample data for network training.
  • the more refined deep learning network puts forward more stringent requirements on the learning ability of the network itself and the computing power of the device, and it is difficult to achieve a balance between calculation and accuracy.
  • Embodiments of the present disclosure provide an image scene segmentation method, device, device, and storage medium, so as to realize optimized processing of scene segmentation results and reduce fragmentation of scene segmentation results.
  • the embodiment of the present disclosure provides an image scene segmentation method, the method
  • a target scene segmentation map of the target image is obtained by performing segmentation correction on the segment block to be processed.
  • an image scene segmentation device which includes:
  • the initial processing module is configured to obtain an intermediate scene segmentation map by performing initial scene segmentation and scene initial fusion processing on the acquired target image;
  • An information determination module configured to detect the segmentation block to be processed from the intermediate scene segmentation map
  • the segmentation correction module is configured to obtain a target scene segmentation map of the target image by performing segmentation correction on the block to be processed.
  • an embodiment of the present disclosure further provides an electronic device, which includes:
  • storage means configured to store at least one program
  • the at least one processor When the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the image scene segmentation method provided in any embodiment of the present disclosure.
  • an embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the image scene segmentation method provided in any embodiment of the present disclosure is implemented.
  • FIG. 1 is a schematic flowchart of an image scene segmentation method provided by Embodiment 1 of the present disclosure
  • FIG. 2 is a schematic flowchart of an image scene segmentation method provided in Embodiment 2 of the present disclosure
  • FIG. 2a shows a schematic structural diagram of a scene segmentation network model used in an image scene segmentation method provided in Embodiment 2 of the present disclosure for initial scene segmentation;
  • Fig. 2b is an implementation flow of image fusion processing in the image scene segmentation method provided in Embodiment 2 of the present disclosure road map;
  • Fig. 2c shows the effect display diagram of the intermediate scene segmentation figure determined in the image scene segmentation method provided in this embodiment
  • Fig. 2d shows the implementation flow chart of determining the segmentation block to be processed in the image scene segmentation method provided by the second embodiment
  • Figure 2e shows an example diagram of the effect of displaying the determined segmentation blocks to be processed in the same image in this embodiment
  • Fig. 2f shows the implementation flowchart of determining the segmentation layer to which the segmentation block to be processed belongs in the image scene segmentation method provided in the second embodiment
  • Fig. 2g shows the effect display diagram of the target scene segmentation diagram in the image scene segmentation method provided by this embodiment
  • FIG. 3 is a schematic structural diagram of an image scene segmentation device provided in Embodiment 3 of the present disclosure.
  • FIG. 4 is a schematic structural diagram of an electronic device provided by Embodiment 7 of the present disclosure.
  • the term “comprise” and its variations are open-ended, ie “including but not limited to”.
  • the term “based on” is “based at least in part on”.
  • the term “one embodiment” means “at least one embodiment”; the term “another embodiment” means “at least one further embodiment”; the term “some embodiments” means “at least some embodiments.” Relevant definitions of other terms will be given in the description below.
  • Embodiment 1 is a schematic flow diagram of an image scene segmentation method provided by Embodiment 1 of the present disclosure. This embodiment is applicable to the case of image segmentation of acquired images.
  • the method can be executed by an image scene segmentation device, which can be realized by software and/or hardware, and can be configured in a terminal and/or server to implement the image scene segmentation method in the embodiment of the present disclosure.
  • an image scene segmentation method provided in Embodiment 1 includes:
  • the target image may be understood as an image to be subjected to image scene segmentation processing, which may be a scene image captured in real time, or an image frame intercepted in a captured video stream.
  • image scene segmentation processing which may be a scene image captured in real time, or an image frame intercepted in a captured video stream.
  • an initial scene segmentation may be performed on the target image first.
  • the scene segmentation of an image it is equivalent to segmenting the image content contained in the image according to the category of the scene to which it belongs, so that the image content of the same scene category is divided into the same scene layer. For example, all doors or windows appearing in the image can be segmented into the segmented layer whose scene category is doors and windows, and vehicles appearing in the image can be segmented into the segmented layer whose scene category is vehicles.
  • a pre-built scene segmentation network model may be used to perform initial scene segmentation on the target image.
  • the pre-built scene segmentation network model can be regarded as a general scene segmentation model, which can be used for scene segmentation of a variety of different scene categories, but there may not be a suitable setting for the coarse and fine granularity of the divisible scene categories, and it is not specifically limited for applicable application scenarios. Therefore, the initial scene segmentation results obtained through the scene segmentation network model may not be the scene segmentation results required by downstream business applications.
  • the object to be processed by the downstream business application is a group of buildings in the image, where Previously, it was necessary to obtain a scene segmentation map containing only building groups.
  • the scene segmentation result after the initial scene segmentation in this step also contains other scene segmentation blocks, or fragmented segmentation blocks with smaller image areas.
  • the doors and windows on buildings may be segmented independently, and they are not in the same scene as the building segmentation, so it is impossible to obtain an accurate building group segmentation map. Therefore, if only the initial scene segmentation of the target image is performed, the downstream business application cannot obtain effective image information.
  • This scene fusion can be regarded as the initial scene fusion in this embodiment, and the scene segmentation result after the initial scene fusion is recorded as an intermediate scene segmentation map.
  • the initial fusion of images may be achieved by applying a certain fusion rule to the segmentation layers under multiple scene categories included in the initial scene segmentation map.
  • the fusion rule adopted may be to fuse the segmentation layer with a smaller range of scene categories into the segmentation layer corresponding to a larger range of scene categories.
  • an initial scene segmentation map can be obtained, thereby obtaining scene labels corresponding to at least one segmentation layer included in the initial scene segmentation map, and then analyzing whether there is an attribution relationship between the scene labels, and merging the scene segmentation maps with the attribution association.
  • the scene label of the segmented floor segmentation layer can be a building floor
  • the segmented door and window layer can have a scene label of doors and windows.
  • the scene fusion performed on the initial scene segmentation result in this step can be regarded as an initial scene fusion processing of the scene segmentation result, and the re-formed segmentation layer after scene fusion can constitute a new scene segmentation diagram.
  • This embodiment records the new scene segmentation diagram as an intermediate scene segmentation diagram.
  • the intermediate scene segmentation map in this step can be considered as the initial scene corresponding to the target image.
  • the scene segmentation result after the initial fusion processing of the initial segmentation results which mainly includes the segmentation layer for image content segmentation according to the scene category, that is, the image content included in each segmentation layer can be considered to belong to the same scene category, and the belonged scene category can be considered to have a larger scene category division range.
  • the scene segmentation algorithm used for the initial scene segmentation of the target image cannot guarantee the accuracy of the scene segmentation. Therefore, the image content may be segmented into the wrong scene category, but the above-mentioned initial fusion process cannot eliminate the wrong segmentation of the scene category to which the image content belongs.
  • the scene segmentation if the scene segmentation is correct, its image content area should be a connected area with a large area; if there are other isolated image content areas in the connected area with a large area area, then the other isolated image content area may be a region with abnormal scene segmentation, which is equivalent to an incorrect scene segmentation in the segmentation layer.
  • the above-mentioned erroneous scene segmentation area may be recorded as a segment to be processed, and the determination of the segment to be processed may be determined by performing connected region detection on at least one segmentation layer in the intermediate scene segmentation map.
  • the determination of the segment to be processed may be determined by performing connected region detection on at least one segmentation layer in the intermediate scene segmentation map.
  • a segmentation layer that includes the image content of the same scene category, by scanning the pixels in the segmentation layer, the detection of connected regions of the image can be realized, and the area of each connected region can be determined. If there is a connected region with an area smaller than a certain threshold, this embodiment can use the connected region as a segment block to be processed.
  • the above-mentioned detected segmentation block is equivalent to a segmented block with incorrect scene segmentation.
  • This step can be used to perform segmentation correction on the pending segmentation block to determine the correct segmentation layer to which the pending segmentation block should belong, and merge the pending segmentation block into the correct segmentation layer.
  • the obtained segmentation layer constitutes the target scene segmentation map of the target image.
  • segment correction is performed on the segment block to be processed, and it is determined that the segment block to be processed should actually be returned to
  • One of the implementation methods of the segmented layer to be processed can be described as: performing region expansion on the segmented block to be processed to obtain the segmented extended region of the segmented block to be processed.
  • the segmented extended region there is an overlapping region that overlaps with other determined connected regions on at least one segmented layer; this embodiment can determine which connected region the segmented block to be processed should belong to by using the overlap ratio of other determined connected regions in the overlapping region, and then determine the segmented layer where the connected region belongs.
  • the segmented layer where the connected region belongs can be used as the segmented layer that the segmented block to be processed should actually belong to .
  • an intermediate scene segmentation map is obtained by performing initial scene fusion processing on the acquired target image; afterward, the segmentation block to be optimized can be determined from the intermediate scene segmentation map, and finally the segment block to be processed can be segmented and corrected, so as to obtain the target scene segmentation map of the target image.
  • the key to the scheme provided in this embodiment is to perform fragmentation detection on the segmentation result after image scene segmentation, and detect the fragmented segmentation block for segmentation correction.
  • the corrected segmentation result realizes the unified segmentation of the image content under the same scene category in the target image, reduces the fragmentation of the segmentation block, and achieves the beneficial effect of effectively improving the accuracy of the segmentation result.
  • the acquisition of an intermediate scene segmentation map by performing scene initial segmentation and scene initial fusion processing on the acquired target image may be embodied as: taking the acquired target image as input data, inputting it into a preset scene segmentation network model, and obtaining an output initial scene segmentation map.
  • the initial scene segmentation map includes at least one initial segmentation layer; based on the content label corresponding to the at least one initial segmentation layer, the at least one initial segmentation The layers perform initial fusion of scenes to obtain intermediate scene segmentation maps.
  • the detection of the segment blocks to be processed from the intermediate scene segmentation map may be embodied as: extracting at least one intermediate segment layer included in the intermediate scene segmentation map; By performing connected domain detection on the at least one intermediate segmentation layer, the segmentation block to be processed in the intermediate scene segmentation map is determined.
  • the target scene segmentation map of the target image obtained by correcting the segmentation result of the block to be processed may be embodied as: for each block to be processed, perform region expansion processing on the block to be processed according to a set expansion coefficient to obtain a corresponding segmented expansion area; based on the segmentation expansion area, determine the target segment layer to which the segment block to be processed belongs from the intermediate scene segment map; perform image fusion on the segment block to be processed and the target segment layer; use the fused intermediate scene segment map as the target scene of the target image Split graph.
  • an image scene segmentation method provided in Embodiment 2 includes the following steps:
  • this step provides the logical realization of the initial segmentation of the scene.
  • this step mainly uses a given scene segmentation network model to initially segment the scene, wherein the target image can be directly input into the scene segmentation network model as input data, and the scene segmentation network model can be regarded as a pre-built neural network model with a specific network structure, and the scene segmentation network model used in this step can be formed after iteratively learning and training the neural network model through a preset training sample set.
  • the scene segmentation network model performs feature extraction and operation processing based on network parameters on the input target image, and can output an initial scene segmentation map including at least one initial segmentation layer.
  • the initial segmentation layer in the initial scene segmentation map includes image content belonging to the same scene category, and in order to better distinguish at least one initial segmentation layer included in the initial scene segmentation map, different color assignments can be performed for different segmentation layers.
  • the scene segmentation network model can be regarded as a general scene segmentation model, that is, applicable to various application scenarios that appear in business applications.
  • the scene segmentation network model also includes a hidden layer that actually participates in the scene segmentation process.
  • the hidden layer of the scene segmentation network model includes a set number of residual sub-network models; the set number of residual sub-network models are connected sequentially in a hierarchical order, and there is a residual sub-network model connected to another non- Residual connections of adjacent residual subnetwork models; each residual subnetwork model consists of a convolutional layer, batch normalization layer, and nonlinear activation function layer.
  • the convolution kernel used by the convolution layer in the residual sub-network model can be a 3*3 convolution kernel; the nonlinear activation function used can be a ReLU function; at the same time, there are residual connections in addition to the sequential connections between the residual sub-network models.
  • the above connection is more conducive to the training of the network model.
  • FIG. 2a shows a schematic structural diagram of a scene segmentation network model used in an image scene segmentation method provided in Embodiment 2 of the present disclosure for initial scene segmentation.
  • the scene segmentation network model includes several residual network ResNet basic units, and each ResNet basic unit is composed of a convolution layer of a 3X3 convolution kernel, a batch normalization (BN batchnorm) layer, and a ReLU (a nonlinear activation function) layer.
  • BN batchnorm batch normalization
  • ReLU a nonlinear activation function
  • this step provides the logical implementation of the initial scene fusion.
  • the initial segmentation layer can be regarded as the segmentation layer in the initial scene segmentation map obtained in S201 above; each of the initial segmentation layers contains image content in the same scene category; the content label can be regarded as the scene category label of the initial segmentation layer, and is used to identify the scene category of the image content included in the scene segmentation image; the content label can be obtained together when the initial scene segmentation map is obtained.
  • the scene categories that can be segmented in the initial scene segmentation diagram are relatively diverse, and the coarseness and granularity of the scene categories are not the same. There is a situation that a certain scene category can actually belong to another scene category. However, if the scene category is too finely divided, the corresponding segmentation result may not match the corresponding application scene of the image scene segmentation, so that the validity of the obtained segmentation result cannot be guaranteed.
  • the scene fusion processing in this step can be realized based on the content label of the initial segmentation layer.
  • corresponding scene category fusion rules can be set relative to the application scene, and then a plurality of content tags satisfying the scene category fusion rules can be determined, and their corresponding segmentation layers can be fused to form a new segmentation layer.
  • an intermediate scene segmentation map can be formed based on the segmentation layers formed after the fusion processing.
  • FIG. 2b is a flowchart for implementing image fusion processing in the image scene segmentation method provided in Embodiment 2 of the present disclosure.
  • this embodiment performs initial fusion of scenes on at least one initial segmentation layer based on the content label corresponding to at least one initial segmentation layer, and obtains an intermediate scene segmentation map as follows:
  • the content label of the initial segmentation layer may be extracted from the obtained initial scene segmentation map.
  • the tag category association table is a preset information rule table, which can be set depending on the current application scenario. Relevant technical personnel can determine multiple scene branches matching the application scene by analyzing the requirements of the application scene, and there may be multiple content tags with affiliation or parallel relationship under different scene branches.
  • the content tags associated with the scene branch include at least: ground, flowers, grass, and trees, etc. Therefore, in the tag category association table set for the application scene, one of the records can be expressed as that content tags such as flowers, grass, trees, and ground belong to the scene branch of the ground.
  • the scene branch associated with each content tag can be obtained by searching the tag category association table in this step.
  • the initial segmentation layer corresponding to the ground, flowers, grass, and trees in the initial scene segmentation map can be used for initial scene fusion, and finally the intermediate scene segmentation map can be obtained through this step.
  • FIG. 2c shows an effect display diagram of an intermediate scene segmentation map determined in the image scene segmentation method provided in this embodiment.
  • the intermediate scene segmentation diagram 23 is shown in Figure 2c, and a plurality of intermediate segmentation layers included in the intermediate scene segmentation diagram 23 are also shown.
  • the displayed first layer 231 mainly presents buildings;
  • the displayed second layer 232 mainly presents the ground, and
  • the displayed third layer 233 mainly presents the sky.
  • S204 Determine the segmentation block to be processed in the intermediate scene segmentation map by performing connected domain detection on the at least one intermediate segmentation layer.
  • the connected region detection in this step can be realized by a set connected region detection algorithm, wherein the core of the connected region detection algorithm can be to scan the pixels of the binarized image to determine whether the pixels are in the same region, and then determine the connected regions in the intermediate segmentation layer; and then find out the segmentation block to be processed with abnormal segmentation according to the area of the connected region.
  • Fig. 2d shows a flow chart for realizing the determination of the segmentation blocks to be processed in the image scene segmentation method provided by the second embodiment.
  • the connected domain detection is performed on the at least one intermediate segmentation layer to determine the pending state of the intermediate scene segmentation graph.
  • the specific steps of splitting blocks are as follows:
  • the binarization process may be to assign a pixel value of 0 or 1 to the pixel points in the intermediate segmentation layer.
  • the scanning sequence of the pixel points may be from left to right and from top to bottom; through this scanning step, the pixel value of the pixel points may be determined.
  • the process of detecting connected regions based on pixel values in this embodiment can be performed in real time during the scanning process of pixel values.
  • the detection of connected regions can be described as: if the pixel value of the scanned current pixel point is 0, move to the next pixel point according to the scanning order; if the pixel value of the scanned current pixel point is 1, detect two adjacent pixel points on the left and upper sides of the current pixel point, and then, according to the pixel values and detection marks of these two adjacent pixel points, consider the following four situations:
  • the region with the same mark can be regarded as a connected region through the mark corresponding to each pixel point.
  • at least one connected region included can be determined through the above operations.
  • the area area of the at least one connected area may be determined, and the area area may be represented by the number of pixel values.
  • the segmented blocks to be processed are also determined through connected domain detection in the multiple intermediate segmented layers shown, such as the connected areas in the first rectangular frame 234 in the first layer 231; the connected areas in the second rectangular frame 235 in the second image 232 can be equivalent to the determined segmented blocks to be processed.
  • Fig. 2e provides an example diagram of the effect of displaying the determined plurality of segmentation blocks to be processed in the same image in this embodiment; as shown in Fig. 2e, the image 24 in Fig. 2e includes a plurality of segmentation blocks to be processed detected from the middle scene segmentation map 23 corresponding to Fig. 2c above.
  • S205 and S206 of this embodiment provide a specific implementation of performing segmentation correction on the segmented block to be processed.
  • the set expansion coefficient may be a matrix with a convolution kernel of 3*3 all 1s, and the block to be processed that participates in the expansion is the expansion center, and then the block to be processed is expanded with a matrix of all 1s of 3*3 to the surroundings.
  • the area after expansion may be recorded as the segmented expansion area.
  • the segmentation and expansion area may be only a peripheral expansion area that expands to the surroundings and does not include the segmentation block to be processed; it may also be a fusion including the segmentation block to be processed and the peripheral expansion area.
  • segmented expansion area corresponding to the detected segmentation block to be processed may overlap with any intermediate segmentation layer in the intermediate scene segmentation map. This step is based on the segmentation expansion area and The overlap ratio of any intermediate segmentation layer can determine which intermediate segmentation layer the segment to be processed belongs to.
  • FIG. 2f shows an implementation flowchart of determining the segmentation layer to which the segmentation block to be processed belongs in the image scene segmentation method provided in the second embodiment.
  • this embodiment will determine the target segmentation layer to which the segment block to be processed belongs to from the intermediate scene segmentation map based on the segmented expansion area as the following steps:
  • the maximum number of pixels corresponds to the largest number of overlapping pixels in the segmented expansion region in the target segmented layer.
  • pixel values of pixels corresponding to multiple image contents in the same segmented layer are the same.
  • one of the image fusion methods can be described as equating the pixel values of multiple pixel points in the segmented block to be processed with the pixel values of the pixel points in the target segmented layer.
  • the fusion processing in this step is equivalent to the scene fusion with the target segmentation layer when the segment block to be processed is subjected to segmentation correction. In this way, the abnormal segmentation repair of the segmentation block to be processed is realized, and the number of fragmented segmentation blocks on at least one segmentation layer in the finally obtained target scene segmentation map is significantly reduced.
  • FIG. 2g shows an effect display diagram of the target scene segmentation diagram in the image scene segmentation method provided in this embodiment.
  • the rendered effect map corresponds to the above-mentioned FIG. 2c.
  • the target scene segmentation map 25 is shown in FIG. 2g, and multiple target segmentation layers included in the target scene segmentation map 25 are also shown. It can be seen that the displayed fourth layer 251 mainly presents the buildings; the displayed fifth layer 252 mainly presents the ground, and the displayed sixth layer 253 mainly presents the sky.
  • the second embodiment provides an image scene segmentation method, which provides the initial scene segmentation of the image through the scene segmentation network module and the initial scene fusion of the initial segmentation result to realize the first segmentation result processing; at the same time, it also provides the specific implementation of detecting the segmentation block to be processed, and also provides the specific implementation of segmentation correction for the segmentation block to be processed.
  • the key to the scheme provided in this embodiment is to perform fragmentation detection on the segmentation result after image scene segmentation, and detect the fragmented segmentation block for segmentation correction.
  • the corrected segmentation result realizes the unified segmentation of the image content under the same scene category in the target image, reduces the fragmentation of the segmentation block, and achieves the beneficial effect of effectively improving the accuracy of the segmentation result.
  • Embodiment 3 is a schematic structural diagram of an image scene segmentation device provided by Embodiment 3 of the present disclosure. This embodiment is applicable to the case of image segmentation of acquired images.
  • the device can be implemented by software and/or hardware, and can be configured in a terminal and/or server to implement the image scene segmentation method in the embodiment of the present disclosure.
  • the device may include: an initial processing module 31 , an information determination module 32 and a segmentation correction module 33 .
  • the initial processing module 31 is configured to obtain an intermediate scene segmentation map by performing initial scene segmentation and scene initial fusion processing on the acquired target image;
  • the information determination module 32 is configured to detect the segmentation block to be processed from the intermediate scene segmentation map
  • the segmentation correction module 33 is configured to obtain a target scene segmentation map of the target image by performing segmentation correction on the block to be processed.
  • the image scene segmentation device provided in the third embodiment solves the problem that the image scene segmentation method in the related art cannot achieve accurate segmentation and produces more fragmented segmentation results.
  • the key to the scheme provided in this embodiment is to perform fragmentation detection on the segmentation result after image scene segmentation, and detect the fragmented segmentation block for segmentation correction.
  • the corrected segmentation result realizes the unified segmentation of the image content under the same scene category in the target image, reduces the fragmentation of the segmentation block, and achieves the beneficial effect of effectively improving the accuracy of the segmentation result.
  • the initial processing module 31 includes:
  • the initial segmentation unit is configured to use the acquired target image as input data, input it to a preset scene segmentation network model, and obtain an output initial scene segmentation map, and the initial scene segmentation map includes at least one initial segmentation layer;
  • the initial fusion unit is configured to perform initial scene fusion on the at least one initial segmentation layer based on the content label corresponding to the at least one initial segmentation layer, to obtain an intermediate scene segmentation map.
  • the initial fusion unit may be set as:
  • the hidden layer of the scene segmentation network model includes a set number of residual sub-network models; the set number of residual sub-network models are connected sequentially in a hierarchical order, and there is a residual sub-network model to another Residual connections of non-adjacent residual subnetwork models; each residual subnetwork model consists of a convolutional layer, batch normalization layer, and nonlinear activation function layer.
  • the information determination module 32 may include:
  • An information extraction unit configured to extract at least one intermediate segmentation layer included in the intermediate scene segmentation map
  • the information determination unit is configured to determine the segmentation blocks to be processed of the intermediate scene segmentation graph by performing connected domain detection on the at least one intermediate segmentation layer.
  • the information determination unit may be configured to: perform binarization processing on each intermediate segmented layer to obtain a corresponding binarized segmented layer; for each binarized segmented layer, perform pixel value scanning on the binarized segmented layer according to a set scanning order; determine connected regions included in the binarized segmented layer according to the pixel value scanning results; and use connected regions whose area area is smaller than a set area threshold as a segment block to be processed.
  • the segmentation correction module may include:
  • the area determination unit is configured to, for each segment block to be processed, perform area expansion processing on the segment block to be processed according to a set expansion coefficient, so as to obtain a corresponding segmented expansion area;
  • the first correction unit is configured to determine the target segmentation layer to which the segmentation block to be processed belongs from the intermediate scene segmentation map based on the segmentation expansion area;
  • the second correction unit is configured to perform image fusion on the segmented block to be processed and the target segmented layer;
  • the target determination unit is configured to use the fusion-processed intermediate scene segmentation map as the target scene segmentation map of the target image.
  • the second correction unit may be set to:
  • the candidate segmentation layer included in the intermediate scene segmentation map, and determine the There is at least one candidate segmentation layer that overlaps in the cut expansion area; the number of pixels in the overlapping area with each of the candidate segmentation layers is counted; the candidate segmentation layer corresponding to the maximum number of pixels is used as the target segmentation layer to which the segmentation block to be processed belongs.
  • the above-mentioned device can execute the method provided by any embodiment of the present disclosure, and has corresponding functional modules for executing the method.
  • FIG. 4 is a schematic structural diagram of an electronic device provided by Embodiment 7 of the present disclosure.
  • the terminal device in the embodiments of the present disclosure may include, but not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, personal digital assistants (Personal Digital Assistant, PDA), PAD (tablet computer), portable multimedia players (Portable Media Player, PMP), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital televisions (Television, TV), desktop computers, etc.
  • PDA Personal Digital Assistant
  • PAD tablet computer
  • portable multimedia players Portable Media Player
  • vehicle-mounted terminals such as vehicle-mounted navigation terminals
  • fixed terminals such as digital televisions (Television, TV), desktop computers, etc.
  • the electronic device shown in FIG. 4 is only an example, and should not limit the functions and application scope of the embodiments of the present disclosure.
  • the electronic device 40 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 41, which may perform various appropriate actions and processes according to a program stored in a read-only memory (Read-Only Memory, ROM) 42 or a program loaded from a storage device 48 into a random access memory (Random Access Memory, RAM) 43.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • the processing device 41, the ROM 42 and the RAM 43 are connected to each other by a bus 45.
  • An input/output (Input/Output, I/O) interface 44 is also connected to the bus 45 .
  • the following devices can be connected to the I/O interface 44: including, for example, a touch screen, touch pad, keyboard, Input device 46 such as mouse, camera, microphone, accelerometer, gyroscope; Comprise such as liquid crystal display (Liquid Crystal Display, LCD), output device 47 such as loudspeaker, vibrator; Comprise such as storage device 48 such as magnetic tape, hard disk; And communication device 49.
  • the communication means 49 may allow the electronic device 40 to communicate with other devices wirelessly or by wire to exchange data. While FIG. 4 shows electronic device 40 having various means, it should be understood that implementing or possessing all of the means shown is not a requirement. More or fewer means may alternatively be implemented or provided.
  • embodiments of the present disclosure include a computer program product, which includes a computer program carried on a non-transitory computer readable medium, where the computer program includes program code for executing the method shown in the flowchart.
  • the computer program may be downloaded and installed from a network via communication means 49, or from storage means 48, or from ROM 42.
  • the processing device 41 When the computer program is executed by the processing device 41, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are executed.
  • the electronic device provided by the embodiment of the present disclosure belongs to the same inventive concept as the image scene segmentation method provided by the above embodiment, and the technical details not described in detail in this embodiment can be referred to the above embodiment.
  • An embodiment of the present disclosure provides a computer storage medium, on which a computer program is stored, and when the program is executed by a processor, the image scene segmentation method provided in the foregoing embodiments is implemented.
  • the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two.
  • a computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof.
  • Computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer disks, hard disks, random Access memory (RAM), read-only memory (ROM), erasable programmable read-only memory ((Erasable Programmable Read-Only Memory, EPROM) or flash memory), optical fiber, portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
  • the program code contained on the computer readable medium may be transmitted by any suitable medium, including but not limited to: electric wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
  • the client and the server can communicate using any currently known or future-developed network protocols such as HTTP (HyperText Transfer Protocol, Hypertext Transfer Protocol), and can be interconnected with any form or medium of digital data communication (for example, a communication network).
  • HTTP HyperText Transfer Protocol
  • Examples of communication networks include local area networks (Local Area Networks, LANs), wide area networks (Wide Area Networks, WANs), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
  • the above-mentioned computer-readable medium may be included in the above-mentioned electronic device, or may exist independently without being incorporated into the electronic device.
  • the above-mentioned computer-readable medium carries at least one program, and when the above-mentioned at least one program is executed by the electronic device, the electronic device:
  • a target scene segmentation map of the target image is obtained by performing segmentation correction on the segment block to be processed.
  • Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, or combinations thereof, including but not limited to object-oriented programming languages—such as Java, Smalltalk, C++, and conventional procedural programming languages—such as the “C” language or similar programming languages.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).
  • LAN local area network
  • WAN wide area network
  • Internet service provider e.g., via the Internet using an Internet service provider
  • each block in the flowchart or block diagram may represent a module, program segment, or portion of code that includes one or more executable instructions for implementing specified logical functions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • the units involved in the embodiments described in the present disclosure may be implemented by software or by hardware. Wherein, the name of the unit does not constitute a limitation of the unit itself under certain circumstances, for example, the first obtaining unit may also be described as "a unit for obtaining at least two Internet Protocol addresses".
  • exemplary types of hardware logic components include: Field Programmable Gate Array (Field Programmable Gate Array, FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Parts (ASSP), System on Chip (System on Chip, SOC), Complex Programmable Logic Device (Complex Programmable Logic Device, CPLD), etc. .
  • a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device.
  • a machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium.
  • a machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing.
  • a machine-readable storage medium would include one or more wire-based electrical connections, a portable computer disk, a hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read only memory
  • EPROM or flash memory erasable programmable read only memory
  • CD-ROM compact disk read only memory
  • magnetic storage devices or any suitable combination of the foregoing.
  • Example 1 provides an image scene segmentation method, the method comprising: obtaining an intermediate scene segmentation map by performing scene initial segmentation and scene initial fusion processing on the acquired target image; detecting the segmentation block to be processed from the intermediate scene segmentation image; and obtaining the target scene segmentation map of the target image by performing segmentation correction on the pending segmentation block.
  • Example 2 provides an image scene segmentation method.
  • the steps in the method are: performing initial scene segmentation and scene initial fusion processing on the acquired target image to obtain an intermediate scene segmentation map, optionally including: using the acquired target image as input data, inputting it into a preset scene segmentation network model, and obtaining an output initial scene segmentation map.
  • the initial scene segmentation map includes at least one initial segmentation layer;
  • Example 3 provides an image scene segmentation method, Steps in the method: based on the content label corresponding to the at least one initial segmentation layer, perform initial scene fusion on the at least one initial segmentation layer to obtain an intermediate scene segmentation map, optionally including: obtaining the content label of each initial segmentation layer; searching a preset label category association table to determine the scene branch to which the content label belongs; performing image content fusion on the initial segmentation layers belonging to the same scene branch to obtain a fused intermediate scene segmentation map.
  • the hidden layer of the scene segmentation network model includes a set number of residual sub-network models; the set number of residual sub-network models are sequentially connected in a hierarchical order, and there is a residual connection between one residual sub-network model and another non-adjacent residual sub-network model; each residual sub-network model is composed of a convolutional layer, a batch normalization layer and a nonlinear activation function layer.
  • Example 5 provides an image scene segmentation method, the steps in the method are: detecting the segmentation block to be processed from the intermediate scene segmentation map, optionally including: extracting at least one intermediate segmentation layer included in the intermediate scene segmentation map; and determining the pending segmentation block of the intermediate scene segmentation map by performing connected domain detection on the at least one intermediate segmentation layer.
  • Example 6 provides an image scene segmentation method, the steps in the method: by performing connected domain detection on the at least one intermediate segmentation layer, determining the segmentation block to be processed in the intermediate scene segmentation map, including: performing binarization processing on each intermediate segmentation layer to obtain a corresponding binary segmentation layer; for each binary segmentation layer, performing pixel value scanning on the binary segmentation layer according to a set scanning order; determining connected regions included in the binary segmentation layer according to the pixel value scanning results; The connected area with the area threshold is set as the segmentation block to be processed.
  • Example 7 provides an image scene segmentation method.
  • the steps in the method are: to obtain the target scene segmentation map of the target image by correcting the segmentation result of the segment block to be processed.
  • region expansion processing on the segment block to be processed according to a set expansion coefficient to obtain a corresponding segmented expansion area; mark the segmentation layer; perform image fusion on the segmentation block to be processed and the target segmentation layer; use the fused intermediate scene segmentation map as the target scene segmentation map of the target image.
  • Example 8 provides an image scene segmentation method.
  • the steps in the method are: based on the segmented expansion area, determine the target segmented layer to which the segment block to be processed belongs from the intermediate scene segmented map, optionally including: acquiring at least one intermediate segmented layer included in the intermediate scene segmented map, determining at least one candidate segmented layer that overlaps with the segmented expanded area; counting the number of pixels in the overlapping area of each candidate segmented layer; using the candidate segmented layer corresponding to the largest number of pixels as the target segmented layer to which the segmented block to be processed belongs .

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Abstract

本公开实施例公开了一种图像场景分割方法、装置、设备及存储介质，该方法包括：通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图；从中间场景分割图中检测待处理分割块；通过对待处理分割块进行分割校正，获得目标图像的目标场景分割图。

Description

一种图像场景分割方法、装置、设备及存储介质
本公开要求在2022年1月21日提交中国专利局、申请号为202210074188.9的中国专利申请的优先权，该申请的全部内容通过引用结合在本公开中。
技术领域
本公开实施例涉及图像处理技术领域，例如涉及一种图像场景分割方法、装置、设备及存储介质。
背景技术
图像场景分割，作为图像处理的其中一个处理研究方向，主要用来对图像中包括的场景按照场景类别进行场景分离。目前，基于深度学习技术的场景、物体分割近年来取得了比较大的突破。
相关技术中的用于图像场景分割的深度学习网络，在对单类别或者少类别场景、物体进行分割时有明显效果，技术也相对成熟。然而，对于存在多类别场景的图像还无法做到精准分割，往往会产生碎片化的分割结果，如果直接将分割结果应用于下游业务实现，将会影响下游业务的执行效果。
相关技术中的改进方式主要考虑直接对深度学习网络进行优化，以此来优化场景分割结果，但是深度学习网络过度依赖于训练数据集，由于较多的场景类别之间往往存在歧义，导致无法提供精准的样本数据用于网络训练；另外，更精细的深度学习网络对网络本身的学习能力以及设备的运算能力都提出了更苛刻的要求，很难做到计算和精度上的平衡。
发明内容
本公开实施例提供了一种图像场景分割方法、装置、设备及存储介质，以实现对场景分割结果的优化处理，减少场景分割结果的碎片化。
第一方面，本公开实施例提供了一种图像场景分割方法，该方法
通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图；
从所述中间场景分割图中检测待处理分割块；
通过对所述待处理分割块进行分割校正，获得所述目标图像的目标场景分割图。
第二方面，本公开实施例还提供了一种图像场景分割装置，该装置包括：
初始处理模块，设置为通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图；
信息确定模块，设置为从所述中间场景分割图中检测待处理分割块；
分割校正模块，设置为通过对所述待处理分割块进行分割校正，获得所述目标图像的目标场景分割图。
第三方面，本公开实施例还提供了一种电子设备，该电子设备包括：
至少一个处理器；
存储装置，设置为存储至少一个程序，
当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现本公开任意实施例所提供的图像场景分割方法。
第四方面，本公开实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现本公开任意实施例所提供的图像场景分割方法。
附图说明
图1为本公开实施例一所提供的一种图像场景分割方法的流程示意图；
图2为本公开实施例二提供的一种图像场景分割方法的流程示意图；
图2a给出了本公开实施例二所提供一种图像场景分割方法中场景初始分割所采用场景分割网络模型的结构示意图；
图2b为本公开实施例二所提供图像场景分割方法中图像融合处理的实现流 程图；
图2c给出了本实施例所提供图像场景分割方法中所确定中间场景分割图的效果展示图；
图2d给出了本实施例二所提供图像场景分割方法中待处理分割块确定的实现流程图；
图2e给出了本实施例在同一图像中对所确定的待处理分割块进行展示的效果示例图；
图2f给出了本实施例二所提供图像场景分割方法中确定待处理分割块所归属分割图层的实现流程图；
图2g给出了本实施例所提供图像场景分割方法中目标场景分割图的效果展示图；
图3为本公开实施例三提供的一种图像场景分割装置的结构示意图；
图4为本公开实施例七所提供的一种电子设备的结构示意图。
具体实施方式
下面将参照附图更详细地描述本公开的实施例。
应当理解，本公开的方法实施方式中记载的各个步骤可以按照不同的顺序执行，和/或并行执行。此外，方法实施方式可以包括附加的步骤和/或省略执行示出的步骤。本公开的范围在此方面不受限制。
本文使用的术语“包括”及其变形是开放性包括，即“包括但不限于”。术语“基于”是“至少部分地基于”。术语“一个实施例”表示“至少一个实施例”；术语“另一实施例”表示“至少一个另外的实施例”；术语“一些实施例”表示“至少一些实施例”。其他术语的相关定义将在下文描述中给出。
需要注意，本公开中提及的“第一”、“第二”等概念仅用于对不同的装置、模块或单元进行区分，并非用于限定这些装置、模块或单元所执行的功能的顺序或者相互依存关系。需要注意，本公开中提及的“一个”、“多个”的修饰是示意性 而非限制性的，本领域技术人员应当理解，除非在上下文另有明确指出，否则应该理解为“至少一个”。
本公开实施方式中的多个装置之间所交互的消息或者信息的名称仅用于说明性的目的，而并不是用于对这些消息或信息的范围进行限制。
实施例一
图1为本公开实施例一所提供的一种图像场景分割方法的流程示意图，本实施例可适用于对所获取的图像进行图像分割的情况，该方法可以由图像场景分割装置来执行，该装置可以通过软件和/或硬件来实现，可配置于终端和/或服务器中来实现本公开实施例中的图像场景分割方法。
如图1所示，本实施例一提供的一种图像场景分割方法包括：
S101、通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图。
在本实施例中，所述目标图像可理解为待进行图像场景分割处理的图像，其可以为实时捕获的场景图像，也可以是所捕获视频流中截取的图像帧。本步骤中首先可以对目标图像进行场景初始分割。需要说明的是，对于图像的场景分割，其相当于将图像中所包含的图像内容按照所归属的场景类别进行分割，从而将同一场景类别的图像内容分割在同一场景图层内，示例性的，如可以将图像中出现的门或窗户都分割至场景类别为门窗的分割图层内，可以将图像中出现的车辆分割至场景类别为车辆的分割图层内。
在本实施例中，可以采用预先构建的场景分割网络模型对目标图像进行初始的场景分割。需要知道的是，预先构建的场景分割网络模型可看做一个通用场景分割模型，其可用于多种不同场景类别的场景分割，但对可分割场景类别的粗细粒度可能并没有一个合适的设定，也并没有专门针对适用的应用场景进行限定，因此，通过场景分割网络模型获得的场景初始分割结果，可能并不是下游业务应用所需要的场景分割结果。
示例性的，假设下游业务应用所要处理的对象是图像中的建筑物群，在此 之前需要获得仅包含建筑物群的场景分割图，然而，本步骤进行场景初始分割后的场景分割结果还包含有其他场景分割块，或者，具备较小图像区域的碎片化分割块，如，建筑物上的门窗可能进行了独立分割，并没有和楼房分割在同一场景中，无法获得精准的建筑物群分割图。因此，若只进行目标图像的初始场景分割，下游业务应用并不能获得有效的图像信息。
基于此，本步骤在对目标图像进行场景初始分割后，还需要对所获得的初始场景分割结果进行图像的场景融合，该场景融合可看做本实施例中的场景初始融合，并且将进行场景初始融合后的场景分割结果记为中间场景分割图。在本实施例中，可以通过对初始场景分割图中所包括的多个场景类别下的分割图层采用一定的融合规则来实现图像的初始融合。其中，所采用的融合规则可以是将场景类别范围较小的分割图层融合到较大范围场景类别所对应的分割图层中。
示例性的，在进行场景初始分割后，可以获得到初始场景分割图，由此可以获取到初始场景分割图中所包括的至少一个分割图层对应的场景标签，之后可以分析场景标签之间是否存在归属关联，并将存在归属关联的场景分割图进行融合。如，分割出的楼层分割图层，其场景标签可以是建筑楼层，分割出的门窗分割图层，其场景标签可以是门窗，分析建筑楼层与门窗之间的归属关联，就可以发现门窗往往是依赖于建筑物的，即门窗的类别范围小于建筑楼层的类别范围，由此可以将门窗分割图层与建筑楼层分割图层进行融合，形成新的建筑分割图层。
在本实施例中，相对于后续步骤中对场景分割图进一步的处理，本步骤对初始的场景分割结果进行的场景融合，可认为是场景分割结果的一次场景初始融合处理，且场景融合后重新形成的分割图层可以构成新的场景分割图，本实施例记该新的场景分割图为中间场景分割图。
S102、从所述中间场景分割图中检测待处理分割块。
在本实施例中，本步骤的中间场景分割图可认为是对目标图像所对应的初 始分割结果进行初始融合处理后的场景分割结果，其中主要包含了按照场景类别进行图像内容分割的分割图层，即，可认为每个分割图层中所包括的图像内容归属于同一场景类别，且所归属的场景类别可认为其具备较大场景类别划分范围。
可以知道的是，对目标图像进行场景初始分割所采用的场景分割算法，并不能保证场景分割的准确性。由此存在图像内容分割至错误场景类别的情况，而通过上述初始融合处理，并不能消除图像内容所属场景类别的错误分割。
对于中间场景分割图中包含的分割图层而言，在场景分割正确的情况下，其所具备的图像内容区域应该是区域面积较大的连通区域；如果在区域面积较大的连通区域中存在孤立的其他图像内容区域，则该孤立存在的其它图像内容区域大概率可能为一个场景分割异常的区域，即相当于该分割图层中存在错误的场景分割。
本实施例可以将上述错误的场景分割区域记为待处理分割块，而对待处理分割块的确定可以通过对中间场景分割图中的至少一个分割图层进行连通区域检测来确定。示例性的，在包含了同一场景类别图像内容的分割图层中，通过对于分割图层中像素点的扫描，可以实现图像连通区域的检测，且可以确定出每个连通区域的区域面积，如果存在区域面积小于一定阈值的连通区域，本实施例就可以将该连通区域作为一个待处理分割块。
S103、通过对所述待处理分割块进行分割校正，获得所述目标图像的目标场景分割图。
在本实施例中，上述检测出的待处理分割块相当于场景分割错误的分割块，可以通过本步骤对待处理分割块进行分割校正，确定出待处理分割块应该归属的正确分割图层，并将待处理分割块融合至正确的分割图层中。当所有的待处理分割块都通过上述逻辑实现了到所归属正确场景分割图的融合后，所获得的分割图层构成了目标图像的目标场景分割图。
示例性的，对待处理分割块进行分割校正，确定待处理分割块实际应该归 属的分割图层的其中一种实现方式可以描述为：对待处理分割块进行区域扩展，获得待处理分割块的分割扩展区域，分割扩展区域中存在与至少一个分割图层上其他已确定连通区域相重叠的重叠区域；本实施例可以通过其他已确定连通区域在重叠区域中的重叠占比，来确定待处理分割块应该归属于哪个连通区域，进而可以确定出所归属连通区域所在的分割图层，所归属连通区域所在的分割图层就可以作为待处理分割块实际应该归属的分割图层。
本实施例一提供的图像场景分割方法，首先通过对所获取目标图像进行场景初始分割集场景初始融合的处理，获得中间场景分割图；之后可以从中间场景分割图中确定出待优化处理的分割块，最终可以对待处理分割块进行分割校正，从而获得该目标图像的目标场景分割图。上述技术方案解决了相关技术中的图像场景分割方法无法实现精准分割，产生较多碎片化分割结果的问题。区别于传统的改进方案，本实施例所提供方案的关键在于对图像场景分割后的分割结果进行碎片化检测，并检测出碎片化分割块进行分割校正，校正后的分割结果实现了对目标图像中同一场景类别下图像内容的统一性分割，减少了分割块的碎片化，达到了有效提升分割结果精准性的有益效果。
实施例二
图2为本公开实施例二提供的一种图像场景分割方法的流程示意图，本实施例在本公开实施例中任一可选技术方案的基础上，可选地，可以将通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图具体化为：将获取的目标图像作为输入数据，输入至预设的场景分割网络模型，获得输出的初始场景分割图，所述初始场景分割图中包括至少一个初始分割图层；基于所述至少一个初始分割图层对应的内容标签，对所述至少一个初始分割图层进行场景初始融合，获得中间场景分割图。
同时，可选的，本实施例还可以将从所述中间场景分割图中检测待处理分割块具体化为：提取所述中间场景分割图中包括的至少一个中间分割图层；通 过对所述至少一个中间分割图层进行连通域检测，确定所述中间场景分割图的待处理分割块。
此外，可选的，本实施例也可以将通过对所述待处理分割块进行分割结果校正，获得所述目标图像的目标场景分割图具体化为：针对每个待处理分割块，按照设定的膨胀系数对所述待处理分割块进行区域膨胀处理，获得相应的分割膨胀区域；基于所述分割膨胀区域，从所述中间场景分割图中确定所述待处理分割块归属的目标分割图层；将所述待处理分割块与所述目标分割图层进行图像融合；将融合处理后的中间场景分割图作为所述目标图像的目标场景分割图。
如图2所示，本实施例二提供的一种图像场景分割方法，包括如下步骤：
S201、将获取的目标图像作为输入数据，输入至预设的场景分割网络模型，获得输出的初始场景分割图，初始场景分割图中包括至少一个初始分割图层。
在本实施例中，本步骤给出了场景初始分割的逻辑实现。示例性的，本步骤主要通过给定的场景分割网络模型来进行场景初始分割，其中，目标图像可以直接作为输入数据输入场景分割网络模型，而场景分割网络模型可认为是预先构建的具备特定网络结构的神经网络模型，通过预先设置的训练样本集对神经网络模型进行迭代学习和训练后，就可以形成本步骤所采用的场景分割网络模型。场景分割网络模型对所输入的目标图像进行特征提取以及基于网络参数的运算处理，可以输出包含至少一个初始分割图层的初始场景分割图。
可以知道的是，初始场景分割图中的初始分割图层中包括了属于同一场景类别的图像内容，为更好区别初始场景分割图中包括的至少一个初始分割图层，可以为不同分割图层进行不同的颜色赋值。
在本实施例中，所述场景分割网络模型可以看作一个通用的场景分割模型，即，可适用于业务应用中出现的各种应用场景。该场景分割网络模型中除输入层和输出层，还包括实际参与场景分割处理的隐藏层。可选的，所述场景分割网络模型的隐藏层包括设定数量的残差子网络模型；所述设定数量的残差子网络模型之间按照层级顺序依次连接，同时存在一个残差子网络模型到另一个非 邻接残差子网络模型的残差连接；每个残差子网络模型由一个卷积层、批量归一化层以及非线性激活函数层组成。
在本实施例中，残差子网络模型中的卷积层所采用的卷积核可以是3*3卷积核；所采用的非线性激活函数可以是ReLU函数；同时，残差子网络模型之间顺序连接外，还存在残差连接。当场景分割网络模型的网络结构相对较深时，通过上述连接，就更有利于网络模型的训练。
示例性的，图2a给出了本公开实施例二所提供一种图像场景分割方法中场景初始分割所采用场景分割网络模型的结构示意图。如图2a所示，该场景分割网络模型的包括若干个残差网络ResNet基本单元，而每个ResNet基本单元都是由3X3卷积核的卷积层，批量归一化(BN batchnorm)层，ReLU(一种非线性激活函数)层构成，各ResNet基本单元之间存在一条直路连接路径21，此外，还有额外的残差连接路径22。本实施例采用该ResNet基本单元构成的网络模型，相当于作为了图像场景分割的主干部分，通过特征提取就可以计算出目标图像中的场景分割图。
S202、基于至少一个初始分割图层对应的内容标签，对所述至少一个初始分割图层进行场景初始融合，获得中间场景分割图。
在本实施例中，本步骤给出了场景初始融合的逻辑实现。其中，所述初始分割图层可认为是上述S201所获得初始场景分割图中的分割图层；每个所述初始分割图层中包含了处于同一场景类别的图像内容；所述内容标签可以看作该初始分割图层的场景类别标签，用于标识场景分割图像所包括图像内容的场景类别；该内容标签可以在获得初始场景分割图时一并获得。
基于本实施例的上述分析，可知初始场景分割图中可分割出的场景类别比较多样化，场景类别粗细粒度并不相同，存在某个场景类别实际可归属于另一个场景类别的情况，而场景类别的过细划分，所对应的分割结果可能与图像场景分割所对应的应用场景并不匹配，从而无法保证所获得分割结果的有效性。
示例性的，假设业务应用中实际的应用分割场景为进行建筑群与地面和天 空的分割，而所获得的初始场景分割图中分别存在内容标签为花、草、树木的分割图层，此时就相当于分割结果与所需的应用分割场景不匹配。分析可知，花、草、树木实则都为长在地面上的植物，其应该属于地面的一部分，为得到更匹配的分割结果，需要通过本步骤进行场景融合处理。
本步骤中的场景融合处理可以基于初始分割图层的内容标签来实现。示例性的，执行逻辑中可相对应用场景设定相应的场景类别融合规则，之后可以确定出满足场景类别融合规则的多个内容标签，并对其所对应的分割图层进行融合，从而形成新的分割图层，完成场景融合后，可以基于融合处理后形成的分割图层构成中间场景分割图。
可选的，图2b为本公开实施例二所提供图像场景分割方法中图像融合处理的实现流程图。如图2b所示，在上述实施例的基础上，本实施例将基于至少一个初始分割图层对应的内容标签，对至少一个初始分割图层进行场景初始融合，获得中间场景分割图具体化为下述步骤：
S2021、获取每个所述初始分割图层的内容标签。
在本实施例中，可以从所获得的初始场景分割图中提取初始分割图层的内容标签。
S2022、查找预先设定的标签类别关联表，确定所述内容标签归属的场景分支。
在本实施例中，所述标签类别关联表为一个预先设定的信息规则表，其可依赖当前所面向的应用场景设定。相关技术人员通过对应用场景需求的分析，可以确定出匹配该应用场景的多个场景分支，而不同场景分支下可以存在多个存在归属关系或并列关系的内容标签。
示例性的，假设一个场景分支为地面，在一个应用场景中，可认为该场景分支下关联的内容标签至少包括：地面、花、草及树木等，由此，相对该应用场景设定的标签类别关联表中，其中一条记录就可以表示为花、草、树木以及地面这类的内容标签均分别归属于地面这个场景分支。在本实施例中，在获取 初始分割图层的内容标签后，通过本步骤对标签类别关联表的查找，就可以获得的每个内容标签所关联的场景分支。
S2023、将属于同一场景分支的初始分割图层进行图像内容融合，获得融合后的中间场景分割图。
接上述示例描述，假设确定出地面、花、草和树木均归属于地面这个场景分支，那么就可以将初始场景分割图中地面、花、草和树木分别对应的初始分割图层进行场景初始融合，最终通过本步骤获得中间场景分割图。
本实施例下述S203和S204给出了检测待处理分割块的逻辑实现。
示例性的，图2c给出了本实施例所提供图像场景分割方法中所确定中间场景分割图的效果展示图。如图2c所示，为便于更好了解中间场景分割图的细节，图2c中即展示了中间场景分割图23，还展示了中间场景分割图23包括的多个中间分割图层，可以看出所展示的第一图层231中主要呈现了建筑群；所展示的第二图层232中主要呈现了地面，而所展示的第三图层233中主要呈现了天空。
S203、提取中间场景分割图中包括的至少一个中间分割图层。
通过上述S202获得中间场景分割图后，相当于已知了所包括的中间分割图层，本步骤提取所述至少一个中间分割图层。
S204、通过对所述至少一个中间分割图层进行连通域检测，确定中间场景分割图的待处理分割块。
在本实施例中，本步骤的连通域检测可以通过设定的连通域检测算法实现，其中，连通域检测算法的核心可以是对二值化处理后的图像进行像素点扫描，以此来确定像素点是否处于同一区域，进而可以确定出中间分割图层中的连通区域；之后还可以根据连通区域的面积查找出分割异常的待处理分割块。
图2d给出了本实施例二所提供图像场景分割方法中待处理分割块确定的实现流程图。如图2d所示，在上述实施例的基础上，可选的，本实施例将通过对所述至少一个中间分割图层进行连通域检测，确定所述中间场景分割图的待处 理分割块具体为下述步骤：
S2041、对每个中间分割图层进行二值化处理，获得相应的二值化分割图层。
示例性的，二值化处理可以是将中间分割图层中像素点进行像素值为0或1的赋值。
S2042、针对每个二值化分割图层，对所述二值化分割图层按照设定扫描顺序进行像素值扫描。
示例性的，像素点的扫描顺序可以是从左到右以及从上到下；通过该扫描步骤，可以确定出像素点的像素值。
S2043、根据所述像素值扫描结果，确定所述二值化分割图层中包括的连通区域。
示例性的，本实施例基于像素值进行连通区域检测的过程可以在像素值扫描过程中实时进行，连通区域的检测可以描述为：假如所扫描当前像素点的像素值为0，就按照扫描顺序移动到下一个像素点；假如所扫描当前像素点的像素值为1，就检测该当前像素点左边和上边的两个邻接像素点，之后，根据这两个邻接像素点的像素值和检测标记，进行下述4种情况的考虑：
1)两邻接像素点的像素值都为0。此时给该当前像素点一个新的标记(表示一个新的连通域的开始)。
2)两邻接像素点的像素值只有一个为1。此时当前像素点的标记与两邻接像素点中像素值为1的标记相同。
3)两邻接像素点的像素值都为1且标记相同。此时当前像素点的标记也为该标记。
4)两邻接像素点的像素值都为1且标记不同。将两邻接像素点所对应标记中较小标记赋值给当前像素点。
接上述描述，结束像素点扫描后，通过每个像素点对应的标记，就可以将标记相同的区域看作一个连通区域，本实施例通过上述操作可以确定出所包括的至少一个连通区域。
S2044、将区域面积小于设定面积阈值的连通区域作为待处理分割块。
本实施例可以确定所述至少一个连通区域的区域面积，该区域面积可以采用像素值数量表征。通过上述描述，场景分割中，分割异常的分割块往往表现为区域面积较小的独立分割块，由此，本步骤可以将区域面积小于设定面积阈值的连通区域作为待处理分割块。
示例性的，接上述图2c的描述，所展示的多个中间分割图层中经过连通域检测也确定出待处理分割块，如第一图层231中第一矩形框234内的连通区域；第二图像232中第二矩形框235内的连通区域，均可相当于确定出的待处理分割块。
同时，图2e给出了本实施例在同一图像中对所确定多个待处理分割块进行展示的效果示例图；如图2e所示，图2e中图像24包括了从上述图2c所对应中间场景分割图23中检测出的多个待处理分割块，为便于更好识别多个待处理分割块，可以采用不同的颜色值为待处理分割块中像素点进行填色。
本实施例下述S205和S206给出了对待处理分割块进行分割校正的具体实现。
S205、针对每个待处理分割块，按照设定的膨胀系数对待处理分割块进行区域膨胀处理，获得相应的分割膨胀区域。
在本实施例中，所设定的膨胀系数可以是一个卷积核为3*3的全1矩阵，参与膨胀的待处理分割块为膨胀中心，然后对待处理分割块向四周以3*3的全1矩阵进行膨胀，本实施例可以将膨胀后的区域记为分割膨胀区域。其中，分割膨胀区域可以仅是向四周膨胀的不包含待处理分割块的外围膨胀区域；也可以是包括了待处理分割块和外围膨胀区域的融合。
S206、基于分割膨胀区域，从中间场景分割图中确定待处理分割块归属的目标分割图层。
需要理解的是，检测出的待处理分割块，其所对应的分割膨胀区域可以与中间场景分割图中的任意中间分割图层存在交叠，本步骤基于分割膨胀区域与 任意中间分割图层的交叠占比，就可以确定出待处理分割块归属于哪个中间分割图层。
可选的，图2f给出了本实施例二所提供图像场景分割方法中确定待处理分割块所归属分割图层的实现流程图。如图2f所示，在上述实施例的基础上，本实施例将基于所述分割膨胀区域，从所述中间场景分割图中确定所述待处理分割块归属的目标分割图层具体化为下述步骤：
S2061、获取所述中间场景分割图中包括的至少一个中间分割图层，确定与所述分割膨胀区域存在重叠的至少一个候选分割图层。
示例性的，通过分割膨胀区域所包括的多个像素点的像素点位置，以及所述至少一个中间分割图层中所包括图像内容的像素点位置，就可以确定分割膨胀区域与哪些中间分割图层存在交叠，由此将存在交叠的中间分割图层记为候选分割图层。
S2062、统计与每个所述候选分割图层相重叠区域的像素点数量。
S2063、将最大像素点数量对应的候选分割图层，作为所述待处理分割块归属的目标分割图层。
在本实施例中，最大像素点数量相当于分割膨胀区域在目标分割图层中存在交叠的像素点数量最多。
S207、将待处理分割块与目标分割图层进行图像融合。
可以知道的是，可选的，本实施例同一个分割图层中多个图像内容所对应像素点的像素值相同。示例性的，其中一种图像融合方式可描述为将待处理分割块中多个像素点的像素值等同于目标分割图层中像素点所具备的像素值。
S208、将融合处理后的中间场景分割图作为目标图像的目标场景分割图。
在本实施例中，本步骤中的融合处理相当于上述待处理分割块进行分割校正时与目标分割图层的场景融合。由此实现了待处理分割块的异常分割修复，最终获得的目标场景分割图中至少一个分割图层上的碎片化分割块数量明显减少。
示例性的，图2g给出了本实施例所提供图像场景分割方法中目标场景分割图的效果展示图。如图2g所示，为便于更好了解目标场景分割图的细节，其所呈现的效果图与上述图2c相互对应，其中，图2g中即展示了目标场景分割图25，还展示了目标场景分割图25中包括的多个目标分割图层，可以看出所展示的第四图层251中主要呈现了的建筑群；所展示的第五图层252中主要呈现了地面，而所展示的第六图层253中主要呈现了天空。
将图2g与图2c进行比对，可以发现，图2c所呈现第二图层232中第二矩形框235内的碎片化分割块，通过分割校正最终融合至图2g所呈现的第四图层251中，从而实现了建筑群场景的完整化，进而也实现了图2g中所呈现第五图层252内地面场景的准确化。
本实施例二提供的一种图像场景分割方法，给出了通过场景分割网络模块对图像进行场景初始分割以及对初始分割结果通过场景初始融合来实现第一次分割结果处理；同时，还给出了检测待处理分割块具体实现，也给出了对待处理分割块进行分割校正的具体实现。通过本实施例提供的方法，解决了相关技术中的图像场景分割方法无法实现精准分割，产生较多碎片化分割结果的问题。区别于传统的改进方案，本实施例所提供方案的关键在于对图像场景分割后的分割结果进行碎片化检测，并检测出碎片化分割块进行分割校正，校正后的分割结果实现了对目标图像中同一场景类别下图像内容的统一性分割，减少了分割块的碎片化，达到了有效提升分割结果精准性的有益效果。
实施例三
图3为本公开实施例三提供的一种图像场景分割装置的结构示意图，本实施例可适用于对所获取的图像进行图像分割的情况，该装置可以通过软件和/或硬件来实现，可配置于终端和/或服务器中来实现本公开实施例中的图像场景分割方法。该装置可包括：初始处理模块31、信息确定模块32以及分割校正模块33。
其中，初始处理模块31，设置为通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图；
信息确定模块32，设置为从所述中间场景分割图中检测待处理分割块；
分割校正模块33，设置为通过对所述待处理分割块进行分割校正，获得所述目标图像的目标场景分割图。
本实施例三提供的一种图像场景分割装置，解决了相关技术中图像场景分割方法无法实现精准分割，产生较多碎片化分割结果的问题。区别于传统的改进方案，本实施例所提供方案的关键在于对图像场景分割后的分割结果进行碎片化检测，并检测出碎片化分割块进行分割校正，校正后的分割结果实现了对目标图像中同一场景类别下图像内容的统一性分割，减少了分割块的碎片化，达到了有效提升分割结果精准性的有益效果。
在本公开实施例中任一可选技术方案的基础上，可选地，初始处理模块31包括：
初始分割单元，设置为将获取的目标图像作为输入数据，输入至预设的场景分割网络模型，获得输出的初始场景分割图，所述初始场景分割图中包括至少一个初始分割图层；
初始融合单元，设置为基于所述至少一个初始分割图层对应的内容标签，对所述至少一个初始分割图层进行场景初始融合，获得中间场景分割图。
在本公开实施例中任一可选技术方案的基础上，可选地，所述初始融合单元，可以设置为：
获取每个初始分割图层的内容标签；查找预先设定的标签类别关联表，确定所述内容标签归属的场景分支；将属于同一场景分支的初始分割图层进行图像内容融合，获得融合后的中间场景分割图。
在本公开实施例中任一可选技术方案的基础上，可选地，所述所述场景分割网络模型的隐藏层包括设定数量的残差子网络模型；所述设定数量的残差子网络模型之间按照层级顺序依次连接，同时存在一个残差子网络模型到另一个 非邻接残差子网络模型的残差连接；每个残差子网络模型由一个卷积层、批量归一化层以及非线性激活函数层组成。
在本公开实施例中任一可选技术方案的基础上，可选地，信息确定模块32可以包括：
信息提取单元，设置为提取所述中间场景分割图中包括的至少一个中间分割图层；
信息确定单元，设置为通过对所述至少一个中间分割图层进行连通域检测，确定所述中间场景分割图的待处理分割块。
在本公开实施例中任一可选技术方案的基础上，可选地，所述信息确定单元可以设置为：对每个中间分割图层进行二值化处理，获得相应的二值化分割图层；针对每个二值化分割图层，对所述二值化分割图层按照设定扫描顺序进行像素值扫描；根据所述像素值扫描结果，确定所述二值化分割图层中包括的连通区域；将区域面积小于设定面积阈值的连通区域作为待处理分割块。
在本公开实施例中任一可选技术方案的基础上，可选地，分割校正模块可以包括：
区域确定单元，设置为针对每个待处理分割块，按照设定的膨胀系数对所述待处理分割块进行区域膨胀处理，获得相应的分割膨胀区域；
第一校正单元，设置为基于所述分割膨胀区域，从所述中间场景分割图中确定所述待处理分割块归属的目标分割图层；
第二校正单元，设置为将所述待处理分割块与所述目标分割图层进行图像融合；
目标确定单元，设置为将融合处理后的中间场景分割图作为所述目标图像的目标场景分割图。
在本公开实施例中任一可选技术方案的基础上，可选地，所述第二校正单元可以设置为：
获取所述中间场景分割图中包括的至少一个中间分割图层，确定与所述分 割膨胀区域存在重叠的至少一个候选分割图层；统计与每个所述候选分割图层相重叠区域的像素点数量；将最大像素点数量对应的候选分割图层，作为所述待处理分割块归属的目标分割图层。
上述装置可执行本公开任意实施例所提供的方法，具备执行方法相应的功能模块。
值得注意的是，上述装置所包括的各个单元和模块只是按照功能逻辑进行划分的，但并不局限于上述的划分，只要能够实现相应的功能即可；另外，各功能单元的具体名称也只是为了便于相互区分，并不用于限制本公开实施例的保护范围。
实施例四
图4为本公开实施例七所提供的一种电子设备的结构示意图。下面参考图4，其示出了适于用来实现本公开实施例的电子设备(例如图4中的终端设备或服务器)40的结构示意图。本公开实施例中的终端设备可以包括但不限于诸如移动电话、笔记本电脑、数字广播接收器、个人数字助理(Personal Digital Assistant，PDA)、PAD(平板电脑)、便携式多媒体播放器(Portable Media Player，PMP)、车载终端(例如车载导航终端)等等的移动终端以及诸如数字电视(Television，TV)、台式计算机等等的固定终端。图4示出的电子设备仅仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图4所示，电子设备40可以包括处理装置(例如中央处理器、图形处理器等)41，其可以根据存储在只读存储器(Read-Only Memory，ROM)42中的程序或者从存储装置48加载到随机访问存储器(Random Access Memory，RAM)43中的程序而执行各种适当的动作和处理。在RAM 43中，还存储有电子设备40操作所需的各种程序和数据。处理装置41、ROM 42以及RAM 43通过总线45彼此相连。输入/输出(Input/Output，I/O)接口44也连接至总线45。
通常，以下装置可以连接至I/O接口44：包括例如触摸屏、触摸板、键盘、 鼠标、摄像头、麦克风、加速度计、陀螺仪等的输入装置46；包括例如液晶显示器(Liquid Crystal Display，LCD)、扬声器、振动器等的输出装置47；包括例如磁带、硬盘等的存储装置48；以及通信装置49。通信装置49可以允许电子设备40与其他设备进行无线或有线通信以交换数据。虽然图4示出了具有各种装置的电子设备40，但是应理解的是，并不要求实施或具备所有示出的装置。可以替代地实施或具备更多或更少的装置。
特别地，根据本公开的实施例，上文参考流程图描述的过程可以被实现为计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在非暂态计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信装置49从网络上被下载和安装，或者从存储装置48被安装，或者从ROM 42被安装。在该计算机程序被处理装置41执行时，执行本公开实施例的方法中限定的上述功能。
本公开实施方式中的多个装置之间所交互的消息或者信息的名称仅用于说明性的目的，而并不是用于对这些消息或信息的范围进行限制。
本公开实施例提供的电子设备与上述实施例提供的图像场景分割方法属于同一发明构思，未在本实施例中详尽描述的技术细节可参见上述实施例。
实施例五
本公开实施例提供了一种计算机存储介质，其上存储有计算机程序，该程序被处理器执行时实现上述实施例所提供的图像场景分割方法。
需要说明的是，本公开上述的计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质或者是上述两者的任意组合。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机 访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器((Erasable Programmable Read-Only Memory，EPROM)或闪存)、光纤、便携式紧凑磁盘只读存储器(Compact Disc Read-Only Memory，CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本公开中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。而在本公开中，计算机可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读信号介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：电线、光缆、射频(Radio Frequency，RF)等等，或者上述的任意合适的组合。
在一些实施方式中，客户端、服务器可以利用诸如HTTP(HyperText Transfer Protocol，超文本传输协议)之类的任何当前已知或未来研发的网络协议进行通信，并且可以与任意形式或介质的数字数据通信(例如，通信网络)互连。通信网络的示例包括局域网(Local Area Network，LAN)，广域网(Wide Area Network，WAN)，网际网(例如，互联网)以及端对端网络(例如，ad hoc端对端网络)，以及任何当前已知或未来研发的网络。
上述计算机可读介质可以是上述电子设备中所包含的；也可以是单独存在，而未装配入该电子设备中。
上述计算机可读介质承载有至少一个程序，当上述至少一个程序被该电子设备执行时，使得该电子设备：
通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图；
从所述中间场景分割图中检测待处理分割块；
通过对所述待处理分割块进行分割校正，获得所述目标图像的目标场景分割图。
可以以一种或多种程序设计语言或其组合来编写用于执行本公开的操作的计算机程序代码，上述程序设计语言包括但不限于面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施例中所涉及到的单元可以通过软件的方式实现，也可以通过硬件的方式来实现。其中，单元的名称在某种情况下并不构成对该单元本身的限定，例如，第一获取单元还可以被描述为“获取至少两个网际协议地址的单元”。
本文中以上描述的功能可以至少部分地由一个或多个硬件逻辑部件来执行。 例如，非限制性地，可以使用的示范类型的硬件逻辑部件包括：现场可编程门阵列(Field Programmable Gate Array，FPGA)、专用集成电路(Application Specific Integrated Circuit，ASIC)、专用标准产品(Application Specific Standard Parts，ASSP)、片上系统(System on Chip，SOC)、复杂可编程逻辑设备(Complex Programmable Logic Device，CPLD)等等。
在本公开的上下文中，机器可读介质可以是有形的介质，其可以包含或存储以供指令执行系统、装置或设备使用或与指令执行系统、装置或设备结合地使用的程序。机器可读介质可以是机器可读信号介质或机器可读储存介质。机器可读介质可以包括但不限于电子的、磁性的、光学的、电磁的、红外的、或半导体系统、装置或设备，或者上述内容的任何合适组合。机器可读存储介质的更具体示例会包括基于一个或多个线的电气连接、便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦除可编程只读存储器(EPROM或快闪存储器)、光纤、便捷式紧凑盘只读存储器(CD-ROM)、光学储存设备、磁储存设备、或上述内容的任何合适组合。
根据本公开的至少一个实施例，【示例一】提供了一种图像场景分割方法，该方法包括：通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图；从所述中间场景分割图中检测待处理分割块；通过对所述待处理分割块进行分割校正，获得所述目标图像的目标场景分割图。
根据本公开的至少一个实施例，【示例二】提供了一种图像场景分割方法，该方法中的步骤：通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图，可选的包括：将获取的目标图像作为输入数据，输入至预设的场景分割网络模型，获得输出的初始场景分割图，所述初始场景分割图中包括至少一个初始分割图层；基于所述至少一个初始分割图层对应的内容标签，对所述至少一个初始分割图层进行场景初始融合，获得中间场景分割图。
根据本公开的至少一个实施例，【示例三】提供了一种图像场景分割方法， 该方法中的步骤：基于所述至少一个初始分割图层对应的内容标签，对所述至少一个初始分割图层进行场景初始融合，获得中间场景分割图，可选的包括：获取每个初始分割图层的内容标签；查找预先设定的标签类别关联表，确定所述内容标签归属的场景分支；将属于同一场景分支的初始分割图层进行图像内容融合，获得融合后的中间场景分割图。
根据本公开的至少一个实施例，【示例四】提供了一种图像场景分割方法，可选的，该方法中场景分割网络模型的隐藏层包括设定数量的残差子网络模型；所述设定数量的残差子网络模型之间按照层级顺序依次连接，同时存在一个残差子网络模型到另一个非邻接残差子网络模型的残差连接；每个残差子网络模型由一个卷积层、批量归一化层以及非线性激活函数层组成。
根据本公开的至少一个实施例，【示例五】提供了一种图像场景分割方法，该方法中的步骤：从所述中间场景分割图中检测待处理分割块，可选的包括：提取所述中间场景分割图中包括的至少一个中间分割图层；通过对所述至少一个中间分割图层进行连通域检测，确定所述中间场景分割图的待处理分割块。
根据本公开的至少一个实施例，【示例六】提供了一种图像场景分割方法，该方法中的步骤：通过对所述至少一个中间分割图层进行连通域检测，确定所述中间场景分割图的待处理分割块，包括：对每个中间分割图层进行二值化处理，获得相应的二值化分割图层；针对每个二值化分割图层，对所述二值化分割图层按照设定扫描顺序进行像素值扫描；根据所述像素值扫描结果，确定所述二值化分割图层中包括的连通区域；将区域面积小于设定面积阈值的连通区域作为待处理分割块。
根据本公开的至少一个实施例，【示例七】提供了一种图像场景分割方法，该方法中的步骤：通过对所述待处理分割块进行分割结果校正，获得所述目标图像的目标场景分割图，可选的为：针对每个待处理分割块，按照设定的膨胀系数对所述待处理分割块进行区域膨胀处理，获得相应的分割膨胀区域；基于所述分割膨胀区域，从所述中间场景分割图中确定所述待处理分割块归属的目 标分割图层；将所述待处理分割块与所述目标分割图层进行图像融合；将融合处理后的中间场景分割图作为所述目标图像的目标场景分割图。
根据本公开的至少一个实施例，【示例八】提供了一种图像场景分割方法，该方法中的步骤：基于所述分割膨胀区域，从所述中间场景分割图中确定所述待处理分割块归属的目标分割图层，可选的包括：获取所述中间场景分割图中包括的至少一个中间分割图层，确定与所述分割膨胀区域存在重叠的至少一个候选分割图层；统计与每个候选分割图层相重叠区域的像素点数量；将最大像素点数量对应的候选分割图层，作为所述待处理分割块归属的目标分割图层。
此外，虽然采用特定次序描绘了各操作，但是这不应当理解为要求这些操作以所示出的特定次序或以顺序次序执行来执行。在一定环境下，多任务和并行处理可能是有利的。同样地，虽然在上面论述中包含了如果干具体实现细节，但是这些不应当被解释为对本公开的范围的限制。在单独的实施例的上下文中描述的某些特征还可以组合地实现在单个实施例中。相反地，在单个实施例的上下文中描述的各种特征也可以单独地或以任何合适的子组合的方式实现在多个实施例中。

Claims (11)

  1. 一种图像场景分割方法，包括：
    通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图；
    从所述中间场景分割图中检测待处理分割块；
    通过对所述待处理分割块进行分割校正，获得所述目标图像的目标场景分割图。
  2. 根据权利要求1所述的方法，其中，所述通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图，包括：
    将获取的目标图像作为输入数据，输入至预设的场景分割网络模型，获得输出的初始场景分割图，所述初始场景分割图中包括至少一个初始分割图层；
    基于所述至少一个初始分割图层对应的内容标签，对所述至少一个初始分割图层进行场景初始融合，获得中间场景分割图。
  3. 根据权利要求2所述的方法，其中，所述基于所述至少一个初始分割图层对应的内容标签，对所述至少一个初始分割图层进行场景初始融合，获得中间场景分割图，包括：
    获取每个初始分割图层的内容标签；
    查找预先设定的标签类别关联表，确定所述内容标签归属的场景分支；
    将属于同一场景分支的初始分割图层进行图像内容融合，获得融合后的中间场景分割图。
  4. 根据权利要求2所述的方法，其中，所述场景分割网络模型的隐藏层包括设定数量的残差子网络模型；
    所述设定数量的残差子网络模型之间按照层级顺序依次连接，同时存在一个残差子网络模型到另一个非邻接残差子网络模型的残差连接；
    每个残差子网络模型由一个卷积层、批量归一化层以及非线性激活函数层组成。
  5. 根据权利要求1所述的方法，其中，所述从所述中间场景分割图中检测 待处理分割块，包括：
    提取所述中间场景分割图中包括的至少一个中间分割图层；
    通过对所述至少一个中间分割图层进行连通域检测，确定所述中间场景分割图的待处理分割块。
  6. 根据权利要求5所述的方法，其中，所述通过对所述至少一个中间分割图层进行连通域检测，确定所述中间场景分割图的待处理分割块，包括：
    对每个中间分割图层进行二值化处理，获得相应的二值化分割图层；
    针对每个二值化分割图层，对所述二值化分割图层按照设定扫描顺序进行像素值扫描；
    根据所述像素值扫描结果，确定所述二值化分割图层中包括的连通区域；
    将区域面积小于设定面积阈值的连通区域作为待处理分割块。
  7. 根据权利要求1所述的方法，其中，所述通过对所述待处理分割块进行分割结果校正，获得所述目标图像的目标场景分割图，包括：
    针对每个待处理分割块，按照设定的膨胀系数对所述待处理分割块进行区域膨胀处理，获得相应的分割膨胀区域；
    基于所述分割膨胀区域，从所述中间场景分割图中确定所述待处理分割块归属的目标分割图层；
    将所述待处理分割块与所述目标分割图层进行图像融合；
    将融合处理后的中间场景分割图作为所述目标图像的目标场景分割图。
  8. 根据权利要求7所述的方法，其中，所述基于所述分割膨胀区域，从所述中间场景分割图中确定所述待处理分割块归属的目标分割图层，包括：
    获取所述中间场景分割图中包括的至少一个中间分割图层，确定与所述分割膨胀区域存在重叠的至少一个候选分割图层；
    统计与每个候选分割图层相重叠区域的像素点数量；
    将最大像素点数量对应的候选分割图层，作为所述待处理分割块归属的目标分割图层。
  9. 一种图像场景分割装置，包括：
    初始处理模块，设置为通过对所获取目标图像进行场景初始分割及场景初始融合处理，获得中间场景分割图；
    信息确定模块，设置为从所述中间场景分割图中检测待处理分割块；
    分割校正模块，设置为通过对所述待处理分割块进行分割校正，获得所述目标图像的目标场景分割图。
  10. 一种电子设备，包括：
    至少一个处理器；
    存储装置，设置为存储至少一个程序，
    当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如权利要求1-8中任一所述的图像场景分割方法。
  11. 一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1-8中任一所述的图像场景分割方法。
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