WO2019090901A1 - Procédé et appareil de sélection d'affichage d'images, terminal intelligent et support de stockage - Google Patents

Procédé et appareil de sélection d'affichage d'images, terminal intelligent et support de stockage Download PDF

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Publication number
WO2019090901A1
WO2019090901A1 PCT/CN2017/116738 CN2017116738W WO2019090901A1 WO 2019090901 A1 WO2019090901 A1 WO 2019090901A1 CN 2017116738 W CN2017116738 W CN 2017116738W WO 2019090901 A1 WO2019090901 A1 WO 2019090901A1
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Prior art keywords
attribute
image
target
weight vector
matrix
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PCT/CN2017/116738
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English (en)
Chinese (zh)
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罗吉童
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广州视源电子科技股份有限公司
广州视臻信息科技有限公司
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Publication of WO2019090901A1 publication Critical patent/WO2019090901A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G06T7/75—Determining position or orientation of objects or cameras using feature-based methods involving models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/97—Determining parameters from multiple pictures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • G06T2207/30201—Face
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30242—Counting objects in image

Definitions

  • the present invention relates to the field of computer application technologies, and in particular, to a method, an apparatus, an intelligent terminal, and a storage medium for selecting an image display.
  • Intelligent terminals have been widely used in various fields of life and life, such as smart phones, smart tablets, smart conference machines, and intelligent teaching equipment. With the development of technology and people's demand for image capture of smart terminals, many smart terminals are currently available. Two or more cameras for image capture are provided to effectively improve the image quality of the smart terminal.
  • the terminal screen In general, multiple cameras are set to capture the subject from different shooting angles, but the terminal screen often only displays images captured by one of the cameras.
  • an image is captured by a multi-camera intelligent terminal, an image captured by a plurality of cameras is analyzed before the image is displayed on the terminal screen, and an image captured by a certain camera is automatically selected for display to ensure the terminal screen. Display images with better shooting angles.
  • the prior art scheme evaluates each camera according to a set experience value combined with the target position information. Whether the corresponding image is suitable for display.
  • the shortcoming of the existing method is that the setting of the empirical value often participates in more human factors, and thus does not guarantee that the image to be displayed determined according to the set experience value is optimal, thereby affecting the image display on the screen of the terminal. effect.
  • the embodiment of the invention provides a method, a device, an intelligent terminal and a storage medium for selecting an image display, which can effectively improve the display effect of the captured image on the multi-camera intelligent terminal device.
  • an embodiment of the present invention provides a method for selecting an image display, including:
  • an embodiment of the present invention provides a device for selecting an image display, including:
  • An attribute matrix determining module configured to acquire a real scene image currently captured by at least two cameras, and determine a target attribute matrix in each of the real scene images
  • a shooting score determining module configured to determine a shooting score of each of the real-life images according to the target attribute weight vector and each of the target object attribute matrices;
  • the image selection display module is configured to select a real-life image corresponding to the highest shooting score for display.
  • an embodiment of the present invention provides an intelligent terminal, including: at least two cameras, and further includes:
  • One or more processors are One or more processors;
  • a storage device for storing one or more programs
  • the one or more programs are executed by the one or more processors, such that the one or more processors implement a method of selecting an image display provided by an embodiment of the present invention.
  • an embodiment of the present invention provides a computer readable storage medium, where a computer program is stored, and when the program is executed by the processor, the method for selecting an image display provided by the embodiment of the present invention is implemented.
  • device, intelligent terminal and storage medium for image display firstly acquiring real scene images currently captured by at least two cameras, and determining a target target data matrix in each real scene image; and then according to the originally determined target genus weight
  • the vector and each of the target attribute matrixes determine the shooting score of each live image, and finally select the live image with the highest shooting score for display.
  • the selection method, the device, the intelligent terminal and the storage medium of the image display can determine the attribute feature information of the object to be displayed in the image and thereby form the target matrix, and can also effectively determine the image display selection and evaluation required by the training.
  • the attribute weight vector thereby effectively selecting the image to be displayed from the images captured by the plurality of cameras based on the determined attribute weight vector, thereby ensuring the image of the best effect on the terminal screen, thereby improving the shooting of the smart terminal. Functional user experience.
  • FIG. 1 is a schematic flowchart of a method for selecting an image display according to Embodiment 1 of the present invention
  • FIG. 2 is a schematic flowchart of a method for selecting an image display according to Embodiment 2 of the present invention
  • FIG. 2b is a flowchart showing an implementation of updating training of a target attribute weight vector according to Embodiment 2 of the present invention
  • FIG. 3 is a structural block diagram of an apparatus for selecting an image display according to Embodiment 3 of the present invention.
  • FIG. 4 is a schematic structural diagram of hardware of an intelligent terminal according to Embodiment 4 of the present invention.
  • FIG. 1 is a schematic flowchart of a method for selecting an image display according to a first embodiment of the present invention.
  • the method is applicable to selecting one of images captured by each camera for displaying based on an intelligent terminal provided with multiple cameras.
  • the method can be performed by a selection device for image display, wherein the device can be implemented by software and/or hardware and is generally integrated on a smart terminal having an image capture function.
  • the smart terminal may be an electronic device such as a mobile phone, a tablet computer, a notebook, a smart conference machine, or a smart teaching device.
  • a method for selecting an image display according to Embodiment 1 of the present invention includes the following operations:
  • At least two cameras are disposed on the smart terminal, and can simultaneously start and perform image acquisition in real time after entering the image capturing function.
  • the image captured by the camera in real time is recorded as a real scene image, and it can be known that The cameras can capture people or objects in the respective image capturing spaces at the same time, so that this step can acquire the real-life images captured by the cameras at the same time.
  • all the physical information of the image capturing space corresponding to the camera may be included in the acquired real-life image, but the physical information contained in the real-life image must be the target object that the user wants to capture, and the embodiment may preferably identify
  • the real-time image can be used as a subject to be photographed by the user, and then the attribute information of each subject in the real-life image is determined to form a subject attribute matrix of the real-life image.
  • the priority of the person included in the live image face recognition is possible
  • the object in the real-life image can be determined by recognizing the physical information in the real-life image, for example, when it is recognized that a person exists in the real-life image, the person can be determined as a real-life image.
  • the subject is photographed, and when there is no person present, it is considered to recognize whether or not the animal is present and the animal in the live view image is determined as the subject at the time of existence, and then the scene recognized from the live view image can be considered as the subject.
  • the third-party identification library that is acquired in advance may be matched to identify a category to which the physical information included in the real-life image specifically belongs, wherein the third-party identification library may be regarded as a feature information database including feature attribute information possessed by the physical object.
  • the feature classification of each physical object in the real-life image can be determined by feature extraction analysis of each physical object in the real-life image, and thereby the target object in the real-life image is determined, for example, if the third-party recognition library is a face recognition library.
  • the third-party recognition library is a face recognition library.
  • the position attribute information of the object in the real-life image such as the coordinate position in the real-life image and the area occupied in the real-life image, may be determined.
  • the step may form a corresponding target data matrix based on the attribute information of each of the captured targets determined in the real-time image, wherein the number of rows in the target attribute matrix represents a possible presence in the live image
  • the number of shots, the number of columns mainly indicates the number of specific items used to identify the attribute information of the subject.
  • S102 Determine a shooting score of each real-life image according to the target attribute weight vector determined by the training and each of the target attribute matrix, and select a real-life image corresponding to the highest shooting score for display.
  • the most basic selection requirement is that the captured angle of the target object included in the real-life image to be displayed is related to the remaining real-life images. Better than its display. Therefore, in this embodiment, it is necessary to filter the subject in each of the real-life images, and select a real-life image including the subject with the best photographing effect as the real-life image to be displayed.
  • the embodiment is equivalent to determining position attribute information of each target in the real scene image in the real scene image, thereby being based on the target attribute
  • the position attribute information of each target in the matrix is combined with the weight that each attribute item in the position attribute information should occupy to determine the score occupied by the subject in the real-life image, and the result of the score is used to judge the shooting of the corresponding target in the corresponding real-life image. effect.
  • the weights that each attribute item in the position attribute information should occupy can be represented by a vector form, and are recorded as an attribute weight vector, and the number of elements in the attribute weight vector and the position attribute information of the target object are included.
  • the number of attribute items included is the same, and for the element values in the genus weight vector, it needs to be pre-trained to ensure that the value of each element in the determined genus weight vector can better reflect the proportion of each attribute item in the position attribute information. This ensures the accuracy of the calculated live image capture score.
  • the embodiment may perform training learning on the attribute weight vector based on the selected network model in combination with the sample data information, thereby obtaining the target attribute weight vector applicable to the embodiment.
  • the determined target attribute matrix is equivalent to the position attribute information based on each of the objects in the live image, and the attribute weight vector given in the embodiment is suitable for judging the shooting score of each live image. Therefore, the pre-trained target attribute weight vector can be directly combined with the target attribute matrix to obtain a shooting score that can judge the quality of the live image.
  • the real-time image to be displayed may be selected according to the level of the shooting score. In this embodiment, it is preferable that the shooting target in the real-estimating image with the highest shooting score has the best shooting effect. This selects the live image with the highest shooting score for display.
  • a method for selecting an image display according to Embodiment 1 of the present invention firstly acquires a real scene image currently captured by at least two cameras, and determines a target target data matrix in each real scene image; and then according to the originally determined target genus weight vector and Each of the target attribute matrixes determines the shooting score of each live image, and finally selects the live image with the highest shooting score for display.
  • the attribute feature information of the object to be displayed in the image can be determined and thus the target object matrix can be formed, and the attribute weight vector required for the image display selection judgment can be effectively determined by training, thereby based on the determined attribute weight
  • the vector effectively realizes the operation of selecting an image to be displayed from images captured by a plurality of cameras, thereby ensuring a captured image capable of displaying the best effect on the terminal screen, thereby improving the user experience of the shooting function of the smart terminal.
  • FIG. 2 is a schematic flowchart of a method for selecting an image display according to Embodiment 2 of the present invention.
  • the embodiment of the present invention is optimized based on the foregoing embodiment. In the embodiment, the optimization is further increased: determining the target attribute weight vector by setting a network model training according to the sample image set.
  • the implementation determines the target attribute weight vector by setting a network model training according to the sample image set, and is embodied as: acquiring a set number of sample images including the target, and determining each sample.
  • Target attribute information in the image constructing a sample image attribute matrix based on each of the subject attribute information, and determining a standard scoring matrix corresponding to the sample image attribute matrix; according to the sample image attribute matrix and a standard scoring matrix
  • the target attribute weight vector corresponding to the sample image attribute matrix is obtained by linear regression model update.
  • the embodiment further determines a target attribute matrix in each of the real-life images, and is configured to: identify at least one target included in each of the real-life images according to the selected target information identification library. And determining position attribute information of each of the subject objects in the corresponding real-life image; and forming a target attribute matrix of each of the real-life images based on position attribute information of the subject objects included in each of the real-life images.
  • the embodiment further determines, according to the target attribute weight vector determined by the training and each of the target object attribute matrices, a shooting score of each of the real-life images, which is embodied as: a target weight determined according to the training. And a vector and each of the subject attribute matrices, determining a target scoring matrix corresponding to each of the subject attribute matrices; determining a sum of element values in each of the target scoring matrices as a photographing score of the corresponding live image.
  • a method for selecting an image display according to Embodiment 2 of the present invention specifically includes the following operations:
  • the target attribute weight vector on which the live image to be displayed is to be filtered may be specifically implemented by the following S201 to S203.
  • This step specifically implements the acquisition operation of the sample data set required for the target attribute weight vector training.
  • the present embodiment determines the sample image including the target as the sample data set. It can be understood that the target attribute weight vector corresponding to different target objects may be different. Therefore, sample images containing different target objects may be selected in advance to form different sample data sets to determine different target objects.
  • the target attribute weight vector may be selected in advance to form different sample data sets to determine different target objects.
  • this step may select a set number of sample images including person information (including a face that needs to be included, one or more faces may be included), and the above setting
  • the quantity can be determined artificially based on historical experience.
  • the face recognition algorithm can determine the attribute characteristics of the person (equivalent to the subject) in each sample image through the face recognition library, and regard the determined attribute feature as the target of each target.
  • the target attribute information (corresponding to the position attribute information mentioned in the first embodiment).
  • the subject attribute information can pass the coordinate position (abscissa and ordinate) of the subject in the live image, and the width value of a certain part of the subject (eg, when the subject is a person) The width of the face) and the angle of deflection of the subject in the horizontal direction (specifically equivalent to the horizontal deflection angle of the subject with respect to the camera), etc., and each information represents an attribute parameter.
  • the target attribute information of each target object in each sample image may be identified according to a corresponding identification algorithm through a predetermined third-party identification library.
  • S202 Construct a sample image attribute matrix based on each of the target attribute information, and determine a standard scoring matrix corresponding to the sample image attribute matrix.
  • the sample image attribute matrix after acquiring the subject attribute information of the subject in each sample image, the sample image attribute matrix can be constructed.
  • the column information of the sample image attribute matrix may be considered to be formed by the target attribute information of each of the objects, that is, each column in the sample image attribute matrix represents one attribute parameter, and the assumed target attribute information includes
  • the number of columns of the sample image attribute matrix can be regarded as the number of attribute parameters plus one, wherein the added one column can be regarded as an offset attribute parameter, which is specifically used for linear division of data.
  • each row in the sample image attribute matrix corresponds to the object attribute information of one subject
  • the number of rows of the sample image attribute matrix can be regarded as the number of all the targets in the set number of sample images, exemplary If the set number of sample images is 10 and the target is a person, the number of faces included in the 10 sample images can be determined, and the sum of the number of faces in the 10 sample images can be used as the sample image. The total number of rows in the attribute matrix.
  • the target attribute information of each line in the sample image attribute matrix may be scored according to personal preference.
  • a scoring criterion given in this embodiment may be: according to personal preference, if According to the personal preference, referring to the target attribute information of the A line, it is considered that the corresponding target is better than the target corresponding to the Bth line in the sample image, and the score of the Ath line higher than the Bth line can be given.
  • the range of the scoring is preferably set to be in the range of 1 to 100.
  • Another scoring criterion given in this embodiment may be: arbitrarily selecting two sample images, and according to personal preference, if one of the sample images X is considered to be due to another For a sample image Y, it should be ensured that the sum of the scores of all the targets given in the sample image X is higher than the sum of the scores of all the targets of the given sample image Y.
  • each row in the sample image attribute matrix has a score value corresponding thereto, thereby forming a standard scoring matrix with the number of rows equal to the number of rows of the sample image attribute matrix and the number of columns being 1.
  • the present embodiment preferably uses a linear regression network model update training to obtain a target attribute weight vector.
  • the obtained sample image attribute matrix can be regarded as the input data set of the linear regression model, and the standard scoring matrix is used as the standard output of the linear regression model.
  • an initial attribute weight vector of the training to be updated is given, and then, based on the initial weight vector and the sample image attribute matrix, the current scoring matrix of the current iteration output is determined by the linear regression model;
  • the learning parameters and the set error feedback function perform an iterative update on the current scoring matrix; and the iterative update operation is performed again on the basis of the scoring matrix obtained by the iterative update until the iterative end condition is obtained to obtain the target attribute weight vector.
  • FIG. 2b is a flowchart of implementing the update training of the target attribute weight vector in the second embodiment of the present invention.
  • the update of the target genus weight vector includes the following operations:
  • S2031 Acquire an attribute weight vector to be updated and a set learning parameter in the linear regression model.
  • the set initial attribute weight vector may be used as the first iteration of the attribute weight vector to be updated, wherein each vector value in the initial attribute weight vector may be randomly set, and the vector dimension of the attribute weight vector may be considered as being taken.
  • the total number of attribute parameters of the target attribute information is the same.
  • the learning parameter can be considered as an iterative update operation of the attribute weight vector by using a plurality of different parameter values, and is obtained according to a curve formed by the iterative result of the set error feedback function in each iteration, and generally can be selected. The curve drops faster, and the parameter with the best convergence effect takes the value as the learning parameter.
  • the learning parameter is preferably set to 0.3. It can be understood that the obtaining of the above learning parameters may be performed in advance before the step is performed, or a set of learning parameters may be given, and the operation given in FIG. 2b is performed based on each learning parameter, and then the curve result is optimally learned.
  • the target attribute weight vector corresponding to the parameter is determined as the final target attribute weight vector.
  • S2032 Determine an error feedback gradient value corresponding to the attribute weight vector to be updated according to the attribute weight vector to be updated, the standard scoring matrix, the sample image attribute matrix, and the set error feedback function gradient formula.
  • This step is equivalent to an intermediate operation in an iteration, and is specifically used to determine an error feedback gradient value corresponding to the current attribute weight vector to be updated after one calculation.
  • the weight update formula is expressed as: Where w ⁇ represents the updated current attribute weight vector; ⁇ represents the set learning parameter.
  • the updated current attribute weight vector can be obtained according to the error feedback gradient value obtained by the intermediate calculation, the learning parameter, and the current attribute weight vector to be updated, and the current attribute weight vector is equivalent to one iteration training.
  • the update gets the attribute weight vector.
  • This step is specifically equivalent to the setting of the loop iteration.
  • the current attribute weight vector obtained by the previous iteration is first regarded as the new attribute weight vector to be updated required by the current iteration, and then can be returned to S2302 for re-execution until The iteration end condition is met.
  • the iterative end condition may be set according to the number of iterations, or the iteration may be determined according to the convergence of the error feedback function. In this embodiment, it is preferable to set the number of iterations of the end of the iteration to 400.
  • the present embodiment determines the current attribute weight vector determined by the update at the end of the iteration as the desired target attribute weight vector.
  • the foregoing S201 to S203 are equivalent to a pre-processing operation, and are mainly used to obtain a target attribute weight vector according to the image to be displayed. It can be understood that the target attribute weight vector corresponding to different target objects can be acquired based on the above operation. This step is equivalent to the actual application of the image display selection. First, the real-life image captured by at least two cameras on the smart terminal is acquired.
  • the target information identification library in this step is equivalent to the third-party identification library mentioned in the above embodiment, and the specific required third-party identification library can be determined according to the difference of the selected target. It can be understood that when the target is identified, a corresponding recognition algorithm is also needed. Generally, the recognition algorithm can be implemented according to various methods such as image feature extraction and image color analysis or image matching.
  • the position attribute information of each subject in the live image is the same as the subject attribute information of the subject in the sample image, and is composed of the same attribute item.
  • the target attribute matrix of each live image is formed based on the position attribute information of the object included in each of the live images.
  • the present embodiment may separately construct a subject attribute matrix for each real-life image, and the subject-target attribute matrix is specifically composed of position attribute information corresponding to the subject included in the live image. Assuming that the position attribute information is composed of four attribute parameters, an additional set offset parameter is added, and it can be determined that the number of columns of the target attribute matrix is 5, and the number of lines is the number of the objects included in the real image, if If a real-life image contains 4 subjects (such as characters), a 4 ⁇ 5 target attribute matrix can be obtained accordingly.
  • This step can determine the target attribute matrix corresponding to each live image based on the above description.
  • the target weight vector may be matrix-calculated with all the formed target attribute matrices.
  • the dimension of the target weight vector is 5, which is equivalent to a 5 ⁇ 1 matrix, and the target attribute matrix is left-multiplied by the target weight vector.
  • An element value in the target scoring matrix corresponds to a score of a target.
  • S208 Determine a sum of element values in each target scoring matrix as a shooting score of the corresponding real-life image, and select a real-life image corresponding to the highest shooting score for display.
  • the sum of the element values in each target scoring matrix is separately calculated, and determined as the shooting score of the corresponding real-life image, and all the shooting scores are compared, and the real-life image corresponding to the highest shooting score may be preferably selected, and the The live image is displayed on the smart terminal.
  • a method for selecting an image display according to the second embodiment of the present invention specifically increases the training determination operation of the target attribute weight vector, and embodies the update training process of the target attribute weight vector; meanwhile, the target attribute is also embodied.
  • the target attribute weight vector required for image display can be accurately and effectively trained through the network model, thereby determining the global optimality from the images captured by multiple cameras simply and effectively in the case where the captured image data is linearly separable.
  • the image can be displayed to ensure the best possible image on the terminal screen, thereby improving the user experience of the smart terminal shooting function.
  • FIG. 3 is a structural block diagram of an apparatus for selecting an image display according to Embodiment 3 of the present invention.
  • the device is suitable for selecting one of images captured by each camera for performing image capture based on a smart terminal provided with multiple cameras, wherein the device can be implemented by software and/or hardware, and is generally integrated in an image capturing function.
  • the apparatus includes an attribute matrix determination module 31, a shooting score determination module 32, and an image selection display module 33.
  • the attribute matrix determining module 31 is configured to acquire a real scene image currently captured by at least two cameras, and determine a target attribute matrix in each of the real scene images;
  • the shooting score determining module 32 is configured to determine a shooting score of each of the real-life images according to the target attribute weight vector and each of the target object attribute matrices;
  • the image selection display module 33 is configured to select a real-life image corresponding to the highest shooting score for display.
  • the device first acquires a real-life image currently captured by at least two cameras through the attribute matrix determining module 31, and determines a target attribute matrix in each of the real-life images; and then passes the shooting score determination module 32 according to the The target attribute weight vector and each of the target object attribute matrices are determined, and the shooting score of each of the real-life images is determined; finally, the real-time image corresponding to the highest shooting score is selected by the image selection display module 33 for display.
  • a selection device for image display according to Embodiment 3 of the present invention is capable of determining attribute feature information of a target to be displayed in an image and thereby forming a target object matrix, and is also capable of effectively determining an image display selection criterion by training.
  • the attribute weight vector thereby effectively selecting the image to be displayed from the images captured by the plurality of cameras based on the determined attribute weight vector, thereby ensuring the image of the best effect on the terminal screen, thereby improving the shooting of the smart terminal. Functional user experience.
  • the apparatus is further optimized to include a target weight determination module 34 for obtaining a target attribute weight vector by setting a network model training according to the sample image set.
  • the target weight determination module 34 includes:
  • a sample information acquiring unit configured to acquire a sample image of the set target including the target object, and determine target attribute information in each of the sample images
  • a sample matrix construction unit constructs a sample image attribute matrix based on each of the target attribute information, and determines a standard score matrix corresponding to the sample image attribute matrix
  • a weight update determining unit configured to obtain, by the linear regression model update, a target attribute weight vector corresponding to the sample image attribute matrix according to the sample image attribute matrix and the standard scoring matrix.
  • weight update determining unit is specifically configured to:
  • a gradient representing the error feedback function J(w) X represents the sample image attribute matrix
  • w represents the attribute weight vector to be updated
  • y represents a standard scoring matrix
  • the weight update formula is expressed as: Where w ⁇ represents the updated current attribute weight vector; ⁇ represents the set learning parameter.
  • attribute matrix determining module 31 is specifically configured to:
  • the image selection display module 33 is specifically configured to:
  • the smart terminal provided in Embodiment 4 of the present invention includes: at least two cameras 41, a processor 42 and a storage device 43. .
  • the processor in the smart terminal may be one or more.
  • One processor 42 is taken as an example in FIG. 4, and at least two cameras 41 of the smart terminals may be respectively connected to the processor 42 and the storage device by using a bus or other means.
  • 43 is connected, and the processor 42 and the storage device 43 are also connected by a bus or the like, and the connection by a bus is taken as an example in FIG.
  • the processor 42 in the smart terminal can control at least two cameras 41 to perform image capturing at the same time.
  • the live image captured by the at least two cameras 41 can also be stored to the storage device 43 to implement image data. storage.
  • the storage device 43 in the smart terminal is used as a computer readable storage medium for storing one or more programs, and the program may be a software program, a computer executable program, and a module, as shown in the embodiment of the present invention.
  • the program instructions/modules corresponding to the selection method include: an attribute matrix determination module 31, a shooting score determination module 32, and an image selection display module 33).
  • the processor 42 executes various functional applications and data processing of the smart terminal by executing software programs, instructions, and modules stored in the storage device 43, that is, a method for selecting an image display in the above method embodiment.
  • the storage device 43 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to usage of the device, and the like. Further, the storage device 43 may include a high speed random access memory, and may also include a nonvolatile memory such as at least one magnetic disk storage device, flash memory device, or other nonvolatile solid state storage device. In some examples, storage device 43 may further include memory remotely located relative to processor 42 that may be connected to the device over a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the program when the smart terminal includes one or more programs executed by the one or more processors 42, the program performs the following operations:
  • the embodiment of the present invention further provides a computer readable storage medium, where the computer program is stored, and the program is implemented by the control device to implement the image display selection method provided by Embodiment 1 or Embodiment 2 of the present invention.
  • the method includes: acquiring a real-life image currently captured by at least two cameras, and determining a target attribute matrix in each of the real-life images; determining each of the target attribute weight vectors according to the training and each of the target attribute matrixes The shooting score of the real-life image is selected, and the real-life image corresponding to the highest shooting score is selected for display.
  • the present invention can be implemented by software and necessary general hardware, and can also be implemented by hardware, but in many cases, the former is a better implementation. .
  • the technical solution of the present invention which is essential or contributes to the prior art, may be embodied in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk of a computer. , Read-Only Memory (ROM), Random Access Memory (RAM), Flash (FLASH), hard disk or optical disk, etc., including a number of instructions to make a computer device (can be a personal computer)
  • the server, or network device, etc. performs the methods described in various embodiments of the present invention.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé et appareil de sélection d'affichage d'images, un terminal intelligent et un support de stockage. Le procédé comporte les étapes consistant à: obtenir des images de scène réelle actuellement capturées par au moins deux caméras, et déterminer des matrices d'attributs de cibles photographiées dans chacune des images de scène réelle; et déterminer, d'après un vecteur de poids d'attributs de cibles, déterminé par apprentissage, et chacune des matrices d'attributs de cibles photographiées, des scores de photographie des images de scène réelle, et sélectionner une image de scène réelle correspondant au score de photographie le plus élevé en vue d'un affichage. À l'aide du procédé, des informations de caractéristiques d'attributs d'une cible photographiée à afficher dans une image peuvent être déterminées et, une matrice de cible photographiée peut ainsi être formée, et l'affichage d'image peut être déterminé efficacement par apprentissage pour sélectionner un vecteur de poids d'attributs nécessaire à la détermination, de sorte que, d'après le vecteur de poids d'attributs déterminé, une image à afficher est sélectionnée efficacement parmi les images capturées par des caméras multiples pour des opérations, ce qui garantit que l'image photographiée avec le meilleur effet peut être affichée sur l'écran d'un terminal, et améliore ainsi l'agrément d'utilisation d'une fonction de photographie du terminal intelligent.
PCT/CN2017/116738 2017-11-10 2017-12-17 Procédé et appareil de sélection d'affichage d'images, terminal intelligent et support de stockage WO2019090901A1 (fr)

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