CN114782711B - Intelligent risk detection method and system based on image recognition - Google Patents
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Abstract
The invention relates to the field related to information risk retrieval, and discloses an intelligent risk detection method and system based on image identification.
Description
Technical Field
The invention relates to the field related to information risk retrieval, in particular to an intelligent risk detection method and system based on image recognition.
Background
With the rapid development of networks, the expansion of information resources brings great convenience, and simultaneously makes the management of information very difficult, in some industries, image material stealing and reuse often occur, if the image is not effectively and reliably detected and evaluated before being put into use, a series of problems may occur after being used, and adverse effects are caused.
In the prior art, most of risk retrieval and evaluation of images adopt overall characteristics, colors and the like of the images to retrieve, so that network image objects with consistency or high coincidence degree are screened out.
Disclosure of Invention
The invention aims to provide an intelligent risk detection method and system based on image recognition, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
an intelligent risk detection system based on image recognition, comprising:
the spatial feature extraction module is used for carrying out feature segmentation processing on the target image, acquiring a plurality of shape features of the target image and establishing spatial relationship features among the shape features, wherein the shape features are used for representing basic graphic line composition elements of the target image;
the characteristic brightness establishing module is used for acquiring color channel information of the target image in a plurality of shape characteristic demarcating areas, calculating a change value of the color channel information of adjacent areas, and carrying out brightness marking on the shape characteristics according to a preset brightness setting method and the change value, wherein the color channel information is used for representing color mixing information and color brightness information;
the retrieval feature reduction module is used for performing resampling processing on the shape features according to the brightness marks to obtain screening relation features, the resampling processing is used for extracting a plurality of shape features of which the variation values are greater than a preset standard to establish the screening relation features, and the spatial relation features comprise the screening relation features;
and the risk detection and judgment module is used for screening the comparison library according to the screening relation characteristics, acquiring a plurality of comparison images reaching a preset contact ratio, comparing and screening the plurality of comparison images according to the spatial relation characteristics, and generating a risk comparison result, wherein the risk comparison result is used for representing the possibility of infringement or embezzlement of the target image and the corresponding comparison images.
As a further scheme of the invention: still include the segmentation of region module, the segmentation of region module specifically includes:
an object recognition unit, configured to perform recognition of a feature subject and a background environment on the target image and generate a range mark, and if the feature subject is not recognized, the step of generating the range mark is not performed, where the feature subject is used to represent non-background environment picture content in the target image and may be a person or an animal;
and the object segmentation unit is used for segmenting the target image according to the range mark and respectively acquiring a target main body image and a target environment image, wherein the target main body image and the target environment are both used for establishing spatial relationship characteristics and screening to generate a risk comparison result.
As a further scheme of the invention: the feature brightness establishing module includes a brightness marking unit for performing the brightness setting method, the brightness marking unit including:
the span analysis subunit is used for acquiring the change values of a plurality of adjacent areas, acquiring a maximum value and a minimum value of the change values by sorting the size of the change values, and segmenting the span areas of the minimum value and the maximum value to acquire brightness levels of the brightness marks;
and the marking subunit is used for judging the brightness level of the change value of the shape characteristic at the division position of the adjacent area and marking the brightness according to the brightness level of the judgment result.
As a further scheme of the invention: the image feature extraction module includes:
the shape feature acquisition unit is used for automatically segmenting the target image according to the color of the target image to acquire a plurality of image segmentation lines, wherein the image segmentation lines are the shape features and are used for representing basic graphic lines forming the target image, and the automatic segmentation step is only used for segmenting adjacent color blocks with discontinuous change;
and the spatial feature establishing unit is used for analyzing the shape features, randomly selecting a preset number of shape features from various positions in the target image, analyzing the position relationship among the shape features, and generating spatial relationship features based on the position relationship and the shape features.
As a further scheme of the invention: the discontinuous change of the color blocks depends on the sampling color depth of the shape feature acquisition unit and the sampling resolution of the target image, the sampling color depth is in direct proportion to the color types which can be identified by the shape feature acquisition unit, and therefore when the color blocks are of continuous transition gradual change colors, the sampling color depth is in direct proportion to the number of generated image dividing lines under the same sampling resolution.
The embodiment of the invention aims to provide an intelligent risk detection method based on image identification, which comprises the following steps:
performing feature segmentation processing on the target image to obtain a plurality of shape features of the target image, and establishing spatial relationship features among the shape features, wherein the shape features are used for representing basic figure line composition elements of the target image;
acquiring color channel information of the target image in a plurality of shape feature demarcated areas, calculating a change value of the color channel information of adjacent areas, and performing brightness marking on the shape features according to a preset brightness setting method and the change value, wherein the color channel information is used for representing color mixing information and color brightness information;
resampling the shape features according to the lightness marks to obtain screening relation features, wherein the resampling process is used for extracting a plurality of shape features of which the variation values are larger than a preset standard to establish the screening relation features, and the spatial relation features comprise the screening relation features;
and screening a comparison library according to the screening relation characteristics to obtain a plurality of comparison images reaching a preset contact ratio, comparing and screening the plurality of comparison images according to the spatial relation characteristics to generate a risk comparison result, wherein the risk comparison result is used for representing the possibility of infringement or embezzlement of the target image and the corresponding comparison images.
As a further scheme of the invention, the method also comprises the following steps:
identifying a characteristic subject and a background environment of the target image to generate a range mark, and if the characteristic subject is not identified, not executing the step of generating the range mark, wherein the characteristic subject is used for representing the non-background environment picture content in the target image and can be a person or an animal;
and segmenting the target image according to the range mark, and respectively obtaining a target main body image and a target environment image, wherein the target main body image and the target environment are used for establishing spatial relationship characteristics and screening to generate a risk comparison result.
As a further scheme of the invention: the step of marking the brightness of the shape feature according to a preset brightness setting method and the variation value specifically includes:
acquiring the change values of a plurality of adjacent areas, acquiring a maximum value and a minimum value of the change values by sorting the change values, and segmenting a span area of the minimum value and the maximum value to acquire brightness marks of a plurality of levels;
and judging the brightness level of the change value of the shape feature at the division position of the adjacent region, and marking the brightness according to the brightness level of the judgment result.
Compared with the prior art, the invention has the beneficial effects that: through the arrangement of the spatial feature extraction module, the feature brightness establishment module, the retrieval feature reduction module and the risk detection judgment module, the risk retrieval based on the image shape features and the spatial relationship features is realized, meanwhile, based on the establishment of brightness marks, the traditional image retrieval traceability retrieval mode based on color judgment can be effectively replaced, the avoidance of image embezzlement behaviors through the modes of modifying the original image color structure and the like to risk assessment can be reliably avoided, and the reliability of risk detection is greatly improved.
Drawings
Fig. 1 is a block diagram of an intelligent risk detection system based on image recognition.
FIG. 2 is a block diagram of brightness mark units in an intelligent risk detection system based on image recognition.
Fig. 3 is a block diagram of an image feature extraction module in an intelligent risk detection system based on image recognition.
Fig. 4 is a flow chart of an intelligent risk detection method based on image recognition.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of specific embodiments of the present invention is provided in connection with specific embodiments.
As shown in fig. 1, an intelligent risk detection system based on image recognition provided for an embodiment of the present invention includes:
the spatial feature extraction module 100 is configured to perform feature segmentation processing on the target image, acquire a plurality of shape features of the target image, and establish a spatial relationship feature between the plurality of shape features, where the shape features are used to represent basic graphic line composition elements of the target image.
The feature brightness establishing module 300 is configured to obtain color channel information of the target image in a plurality of shape feature demarcated regions, calculate a variation value of the color channel information of adjacent regions, and perform brightness marking on the shape features according to a preset brightness setting method and the variation value, where the color channel information is used to represent color mixture information and color brightness information.
A retrieval feature reduction module 500, configured to perform resampling processing on the shape features according to the brightness marks to obtain a screening relationship feature, where the resampling processing is used to extract a plurality of shape features whose variation values are greater than a preset standard to establish the screening relationship feature, and the spatial relationship feature includes the screening relationship feature.
The risk detection and determination module 700 is configured to screen a comparison library according to the screening relationship features, obtain a plurality of comparison images that reach a preset coincidence degree, compare and screen the plurality of comparison images according to the spatial relationship features, and generate a risk comparison result, where the risk comparison result is used to represent the possibility of infringement or theft of the target image and the corresponding comparison images.
In the embodiment, an intelligent risk detection system is provided, which is used for retrieving image pictures so as to inquire the originality of the pictures, under the background of rapid development of the current network, phenomena such as material embezzlement, material alteration and plagiarism are more and more serious, and the system is very important for the benefit security guarantee of creators; the function of the feature brightness establishing module 300 is to distinguish the lines of the shape features, because on a target image, the number of the shape features that can be generated is large, and therefore a primary and a secondary need to be distinguished for convenient use and search, where the change judgment of the color channel information is adopted, that is, if the color channel information between two adjacent color blocks is very close in value, the brightness is marked as 1 (assuming that the brightness is 1-10 in the range, where 10 is the largest change of the color channel information in the image), if the color difference between two adjacent color blocks is large, the color block is marked as 10, at this time, when the simplification processing is performed, we use the brightness mark as 10 with a better effect, the lines may be the main structure forming lines of the target image, and therefore the shape features are reduced by the search feature reducing module 500 (at the same time, the spatial relationship corresponding to the shape is also deleted), the cuprea and neon risk determination module 700 performs rapid screening and final comparison; compared with the prior art, the processing mode can better perform tracing retrieval on the target image, so that the risk that the target image is embezzled and the content is changed is judged, and particularly for the change of the image, the problem that the prior art cannot effectively retrieve, identify and trace the source can be solved.
As another preferred embodiment of the present invention, the present invention further includes a region dividing module, where the region dividing module specifically includes:
and the object identification unit is used for identifying a characteristic subject and a background environment of the target image to generate a range mark, and if the characteristic subject is not identified, the step of generating the range mark is not executed, wherein the characteristic subject is used for representing the non-background environment picture content in the target image and can be a person or an animal.
And the object segmentation unit is used for segmenting the target image according to the range mark and respectively acquiring a target main body image and a target environment image, wherein the target main body image and the target environment are both used for establishing spatial relationship characteristics and screening to generate a risk comparison result.
In the embodiment, the region division module is supplemented and is explained with functional division, when in use, the main function is to identify and segment the main body element and the background element in the image, for example, when the target image is a cartoon in a composition such as a novel, a game, etc., the target image usually includes a character and a background, and in the prior art, in the search for risk assessment, the search assessment of the overall characteristics is usually adopted, in fact, unless in the case of direct embezzlement, in more cases separate alterations of backgrounds and tasks occur, i.e. backgrounds and characters from different images, eventually combined after simple modification (for example colour matching of the background, a few detailed items; coloration of the garment of the character, etc.), therefore, the existing method for directly retrieving and evaluating is difficult to reliably evaluate, or partial evaluation causes that the evaluation result has no reference.
As shown in fig. 2, as another preferred embodiment of the present invention, the feature brightness establishing module 300 includes a brightness flag unit 301 for executing the brightness setting method, where the brightness flag unit 301 includes:
the span analysis subunit 3011 is configured to obtain the variation values of a plurality of neighboring areas, obtain a maximum value and a minimum value of the variation values by sorting the magnitude of the variation values, and segment the span areas of the minimum value and the maximum value to obtain brightness levels of the brightness marks.
And a marking subunit 3012, configured to perform brightness level determination on the change value of the shape feature at the location where the adjacent area is divided, and perform brightness marking according to the brightness level of the determination result.
In this embodiment, the lightness setting method is described, and in this process, the lightness level is established based on the span between the maximum value and the minimum value of the variation value in the image, and the number of levels is the same.
As shown in fig. 3, as another preferred embodiment of the present invention, the image feature extraction module 100 includes:
a shape feature obtaining unit 101, configured to automatically segment the target image according to the color of the target image, and obtain a plurality of image segmentation lines, where the image segmentation lines are the shape features and are used to represent basic graphic lines forming the target image, and the step of automatically segmenting is only used to segment adjacent color blocks with discontinuous changes.
The spatial feature establishing unit 102 is configured to analyze the shape features, randomly select a preset number of shape features from various places in the target image, analyze a positional relationship between the shape features, and generate a spatial relationship feature based on the positional relationship and the shape features.
Further, the discontinuous variation of the color blocks depends on the sampling color depth of the shape feature obtaining unit 101 and the sampling resolution of the target image, and the sampling color depth is proportional to the color type that can be identified by the shape feature obtaining unit 101, so when the color blocks are transition colors, the sampling color depth is proportional to the number of the generated image dividing lines at the same sampling resolution.
In this embodiment, when performing automatic segmentation to obtain image segmentation lines, that is, shape features, two contents, namely, sampling color depth and sampling resolution, are involved, where the sampling color depth determines the type of color that can be identified, and here, as an extreme example, for a complete natural light spectrum strip, when the sampling color depth is infinite, the sampling result is continuous, and when the sampling color depth becomes very low, the sampling result is a strip-shaped light strip, and a segmentation line (that is, a true color) is generated between adjacent color strips, so that the sampling color depth can change the number of generated shape features to some extent.
As shown in fig. 4, the present invention further provides an intelligent risk detection method based on image recognition, which includes:
s200, performing feature segmentation processing on the target image, acquiring a plurality of shape features of the target image, and establishing spatial relationship features among the shape features, wherein the shape features are used for representing basic graphic line composition elements of the target image.
S400, obtaining color channel information of the target image in a plurality of shape feature demarcated areas, calculating a change value of the color channel information of adjacent areas, and performing brightness marking on the shape features according to a preset brightness setting method and the change value, wherein the color channel information is used for representing color mixing information and color brightness information.
S600, resampling processing is carried out on the shape features according to the lightness marks to obtain screening relation features, the resampling processing is used for extracting a plurality of shape features with the variation values larger than a preset standard to establish the screening relation features, and the spatial relation features comprise the screening relation features.
S800, screening comparison libraries according to the screening relation characteristics to obtain a plurality of comparison images reaching a preset coincidence degree, comparing and screening the plurality of comparison images according to the spatial relation characteristics to generate a risk comparison result, wherein the risk comparison result is used for representing the possibility of infringement or embezzlement of the target image and the corresponding comparison images.
As another preferred embodiment of the present invention, further comprising the steps of:
and identifying a characteristic subject and a background environment of the target image to generate a range mark, and if the characteristic subject is not identified, not executing the step of generating the range mark, wherein the characteristic subject is used for representing the non-background environment picture content in the target image and can be a person or an animal.
And segmenting the target image according to the range mark, and respectively obtaining a target main body image and a target environment image, wherein the target main body image and the target environment are used for establishing spatial relationship characteristics and screening to generate a risk comparison result.
As another preferred embodiment of the present invention, the step of brightness marking the shape feature according to a preset brightness setting method and the variation value specifically includes:
and acquiring the change values of a plurality of adjacent areas, acquiring a maximum value and a minimum value of the change values by sequencing the size of the change values, and segmenting the span areas of the minimum value and the maximum value to acquire brightness marks of a plurality of levels.
And judging the brightness level of the change value of the shape feature at the division position of the adjacent region, and marking the brightness according to the brightness level of the judgment result.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a non-volatile computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (6)
1. An intelligent risk detection system based on image recognition, comprising:
the spatial feature extraction module is used for carrying out feature segmentation processing on a target image, acquiring a plurality of shape features of the target image and establishing spatial relationship features among the shape features, wherein the shape features are used for representing basic graphic line composition elements of the target image;
the characteristic brightness establishing module is used for acquiring color channel information of the target image in a plurality of shape characteristic demarcating areas, calculating a change value of the color channel information of adjacent areas, and carrying out brightness marking on the shape characteristics according to a preset brightness setting method and the change value, wherein the color channel information is used for representing color mixing information and color brightness information;
the retrieval feature reduction module is used for performing resampling processing on the shape features according to the brightness marks to obtain screening relation features, the resampling processing is used for extracting a plurality of shape features of which the variation values are greater than a preset standard to establish the screening relation features, and the spatial relation features comprise the screening relation features;
the risk detection and judgment module is used for screening a comparison library according to the screening relation characteristics to obtain a plurality of comparison images reaching a preset contact ratio, comparing and screening the plurality of comparison images according to the spatial relation characteristics to generate a risk comparison result, and the risk comparison result is used for representing the possibility of infringement or embezzlement of the target image and the corresponding comparison images;
the feature brightness establishing module includes a brightness flag unit for performing the brightness setting method, the brightness flag unit including:
the span analysis subunit is used for acquiring the change values of a plurality of adjacent areas, acquiring a maximum value and a minimum value of the change values by sorting the size of the change values, and segmenting the span areas of the minimum value and the maximum value to acquire brightness levels of the brightness marks;
and the marking subunit is used for judging the brightness level of the change value of the shape characteristic at the division position of the adjacent area and marking the brightness according to the brightness level of the judgment result.
2. The intelligent risk detection system based on image recognition according to claim 1, further comprising a region segmentation module, wherein the region segmentation module specifically comprises:
an object recognition unit, configured to perform recognition of a feature subject and a background environment on the target image and generate a range mark, and if the feature subject is not recognized, the step of generating the range mark is not performed, where the feature subject is used to represent non-background environment picture content in the target image and may be a person or an animal;
and the object segmentation unit is used for segmenting the target image according to the range mark and respectively acquiring a target main body image and a target environment image, wherein the target main body image and the target environment are both used for establishing spatial relationship characteristics and screening to generate a risk comparison result.
3. The intelligent risk detection system based on image recognition according to claim 1, wherein the image feature extraction module comprises:
the shape feature acquisition unit is used for automatically segmenting the target image according to the color of the target image to acquire a plurality of image segmentation lines, wherein the image segmentation lines are the shape features and are used for representing basic graphic lines forming the target image, and the automatic segmentation step is only used for segmenting adjacent color blocks with discontinuous change;
and the spatial feature establishing unit is used for analyzing the shape features, randomly selecting a preset number of shape features from various positions in the target image, analyzing the position relationship among the shape features, and generating spatial relationship features based on the position relationship and the shape features.
4. The intelligent risk detection system based on image recognition of claim 3, wherein the discontinuous variation of the color blocks depends on the sampling color depth of the shape feature obtaining unit and the sampling resolution of the target image, the sampling color depth is proportional to the color types that can be recognized by the shape feature obtaining unit, so when the color blocks are the continuous transition gradient colors, the sampling color depth is proportional to the number of the generated image dividing lines at the same sampling resolution.
5. An intelligent risk detection method based on image recognition is characterized by comprising the following steps:
performing feature segmentation processing on a target image to obtain a plurality of shape features of the target image, and establishing spatial relationship features among the shape features, wherein the shape features are used for representing basic figure line composition elements of the target image;
acquiring color channel information of the target image in a plurality of shape feature demarcated areas, calculating a change value of the color channel information of adjacent areas, and performing brightness marking on the shape features according to a preset brightness setting method and the change value, wherein the color channel information is used for representing color mixing information and color brightness information;
resampling the shape features according to the lightness marks to obtain screening relation features, wherein the resampling process is used for extracting a plurality of shape features of which the variation values are larger than a preset standard to establish the screening relation features, and the spatial relation features comprise the screening relation features;
screening a comparison library according to the screening relation characteristics to obtain a plurality of comparison images reaching a preset coincidence degree, and comparing and screening the plurality of comparison images according to the spatial relation characteristics to generate a risk comparison result, wherein the risk comparison result is used for representing the possibility of infringement or embezzlement of the target image and the corresponding comparison images;
the step of brightness marking the shape feature according to a preset brightness setting method and the variation value specifically includes:
acquiring the change values of a plurality of adjacent areas, acquiring a maximum value and a minimum value of the change values by sorting the change values, and segmenting a span area of the minimum value and the maximum value to acquire brightness marks of a plurality of levels;
and judging the brightness level of the change value of the shape feature at the division position of the adjacent region, and marking the brightness according to the brightness level of the judgment result.
6. The intelligent risk detection method based on image recognition according to claim 5, further comprising the steps of:
identifying a characteristic subject and a background environment of the target image to generate a range mark, and if the characteristic subject is not identified, not executing the step of generating the range mark, wherein the characteristic subject is used for representing the non-background environment picture content in the target image and can be a person or an animal;
and segmenting the target image according to the range mark, and respectively obtaining a target main body image and a target environment image, wherein the target main body image and the target environment are used for establishing spatial relationship characteristics and screening to generate a risk comparison result.
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