# Key Frame Extraction Method for Minors’ Participation in Online Short-Form Video from the Perspective of Government Administration

Authors: Qiuli Wu
Journal: Discrete Dynamics in Nature and Society
 (2022)

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## Abstract

With the progress of the Internet era, the audience scope of online short-form video and live broadcast platform is rapidly expanding. In this situation, the physical and mental health of minors is affected and harmed, and serious social problems have been caused. In view of the random images of minors participating in short online videos, the monitoring department should strengthen supervision and control and establish a good network atmosphere. The focus of surveillance is to identify short-form videos in which minors participate, monitor the content of short-form videos, and effectively identify minors. On the basis of analyzing and studying the existing methods of moving object extraction, the method of spatio-temporal information combined with geometric curve evolution is used to improve the effect of moving object extraction. Firstly, the time dynamic information of video frames is fully utilized and the initial contour of the target is segmented by high-order statistical algorithm. Based on the improved watershed algorithm, the spatial frames of the original image are segmented and the secondary contour of the target is obtained by combining the spatial and temporal information. The level set evolution algorithm is used to remove the noise and information void in the secondary target, and the final moving target is obtained. Through the extraction of key frames, it can effectively control the appearance of minors in short-form videos, which can actively guide the benign development of the platform, and put forward governance suggestions based on the perspective of government management.


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## Body

## 1. Introduction

Short-form video is a kind of short video, which can be 15 seconds, 1 minute, 3 to 5 minutes. Short-form video is a new type of video, which can be shared in real time and seamlessly connected on social media platforms, mainly relying on mobile intelligent terminals to achieve rapid shooting, beautification, and editing. It integrates text, voice, and video, which can more intuitively and stereoscopically meet users’ needs of expression and communication, and meet people’s demands of display and sharing. With the development of the Internet era, the number and scope of Internet short-form video users have been expanding, among which minors account for a considerable proportion. At the same time, the network short-form video also has an important impact on minors. There are a large number of minors in the user group of online short-form video fans in China, but the cognitive ability of minors is weaker than that of adults and their self-discipline is relatively poor. Many web video publishers are using this feature to capture more attention and play. They do not hesitate to lower the moral bottom line, which leads to the status quo of the spread of short-form videos on the Internet, which presents the good and bad, and seriously endangers the social atmosphere. In addition, some publishers induce minors who lack self-control to consume through the reward function of short-form videos on the Internet and instil minors to reward their video content, taking advantage of which they collect large amounts of ill-deserved wealth [1–5].There are a large number of minors in the user group of online short-form video fans in China, but the cognitive ability of minors is weaker than that of adults and their self-discipline is relatively poor. In the contents publicly displayed by minors in short-form videos on the Internet, it can be seen that minor girls reveal sexual innuendo does not match their age through actions and words, and publish daily life of early marriage and pregnancy [6–8]. Underage students engage in online fights and violent fights between each other, showing off their personal behavior of dropping out of school and persuading others, minors in illegal occasions such as high consumption. Moreover, there are minors who spontaneously form so-called combinations of short-form videos on the Internet to release unhealthy video content on a large scale. These moral anomie behaviors are spread in the network short-form video, and even publicized and flaunted, which have deformed the immature three views of minors [7–10].In view of the random images of minors participating in short online videos, the monitoring department should strengthen supervision and control and establish a good network atmosphere. The focus of surveillance is to identify short-form videos in which minors participate, monitor the content of short-form videos, and effectively identify minors. Minors belong to the key frame in the video, which can be effectively controlled by identifying the key frame [11–15]. A key frame is a key image frame that describes a shot, usually reflecting the main content of a shot. Therefore, key frame extraction technology is the basis of video analysis and video retrieval. In this paper, a fuzzy clustering algorithm is used to improve the stability of the application of key frame extraction. Firstly, the time series and dynamic information of the video can be kept through the shot detection of the video clip through the mutual information algorithm, and then the key frames in the shot can reflect the main content of the video shot better by the fuzzy clustering extraction. The initial cluster center and the number of clusters are needed for clustering calculation. In this paper, the density function method is used to determine the initial clustering center. The average entropy method is used to calculate the number of clustering, which ensures the parameterless operation of fuzzy clustering algorithm and the stability of clustering effect. Finally, the experiment proves that the key frame extracted by the system can better represent the video content and is conducive to video analysis and retrieval [16–18].Video moving object extraction is the basis of video semantic analysis and a breakthrough to solve the problem of “semantic gap.” This paper analyzes the current video motion object extraction methods in detail. On this basis, a moving object extraction method using spatio-temporal information combined with geometric curve evolution is adopted to improve the extraction effect of animation object. The final object is extracted through four steps, and the initial moving object space is obtained by high-order statistics algorithm. Aiming at the phenomenon of oversegmentation in watershed algorithm [19–21], the concept of adjacency graph is used to improve it, and the segmentation region and spatio-temporal information of video image frame are obtained. According to the proportion relation between motion and region, reserving information is determined and the quadratic moving object is obtained. The level set method is used to evolve geometric curves to obtain the final moving object. This method makes comprehensive use of time-domain and space-domain information, and retains the information of video image frame comprehensively. After the final evolution analysis, the moving object is better [22–24].Based on the perspective of government management, this paper studies the current situation of minors’ participation in online short videos and purifies the environment for minors’ participation in the Internet. Through the results of key frame extraction, suggestions are put forward to manage network short video, improve the dynamic precision of government network monitoring, and help the government to consolidate and improve its management ability in network space.


## 2. Short-Form Video Structure

### 2.1. Video Hierarchy

Video is the most complex type of multimedia information. It is a comprehensive media information integrating image, sound, and text. It has the advantages of large amount of information and vivid form of expression. The video data can be structurally divided into video sequence, scene, shot, and frame from top to bottom (Figure1). A frame is the smallest unit of video data, a still picture. The lens is the basic unit of video data. It consists of several consecutive frames of images taken continuously by a camera in time. There are two types of camera switching: abrupt change and gradual change. The mutation is a direct transition from one shot to the next. There is no time delay gradient in the middle, but some editing effect in space or time is added, and the previous shot slowly transitions to the next shot. There are mainly fade in and fade out, slow conversion and sweep conversion. A scene consists of scene of similar content, depicting the same event from different angles. Video sequences consist of many scenes and generally tell a complete story.Figure 1
Hierarchical structure of video.As can be seen from Figure1, the higher the hierarchical structure of the video, the richer the content information contained therein, which means the higher the difficulty of processing. Therefore, top-down anatomical analysis is often used for video processing. Firstly, the video sequence is divided into multiple scenes by scene detection, and then each scene is divided into multiple scene by shot segmentation. Then, the key frame of each shot is extracted as the main content of the video sequence. In this process, scene detection, shot segmentation, and key frame extraction are involved because the lens is the basic unit of video data. At present, the more common method is to directly take the video clip as the unit, detect the shot first, and then extract the key frame without scene detection.


### 2.2. Extraction of Short-Form Video Key Frames

Key frame refers to the most important and representative image frame in the lens. It reflects the main content of a shot and is the basis for building a video sequence index. Using key frame technology to query, search, and browse, video database effectively can greatly reduce the amount of video data. It also provides a unified organizational framework for video processing. It also provides a unified organizational framework for video processing. Depending on the complexity of the shot content, one or more key frames can be extracted from a shot. Key frame extraction must be able to reflect the main content information in the shot more accurately. Secondly, the dynamic information of video sequence changes should be preserved to facilitate the indexing and management of video sequence. The traditional manual extraction method has high accuracy, but it is time-consuming and inefficient. At present, automatic detection technology is mainly used to extract key frames. Typical key frame extraction methods mainly include shot boundary-based method, content-based method, motion-based method, and clustering-based method. This paper proposes a key frame extraction method combining mutual information and fuzzy clustering. The method uses information entropy instead of Euclidean distance to calculate the similarity between frames and determines the number of clusters according to the average information entropy. The density function method is used to obtain the initial clustering center, which does not require users to input parameters related to clustering, so that the obtained key frames can not only better represent the main content of the lens, but also have good stability. Meanwhile, the time series and dynamic information of the video are maintained.


### 2.3. Traditional Key Frame Extraction Method

This method is based on shot boundary. In video sequences, shot boundaries are divided into two categories: shear and gradient. A shear is when a change occurs suddenly between two consecutive frames, while a gradient is when a change occurs between consecutive frames. Gradients are complex, including fading in and out, dissolving and erasing. At present, many researchers have proposed a variety of different lens boundary detection methods, such as calculating the pixel difference of the corresponding position of adjacent image frames, algorithm based on histogram difference, and method based on edge information. Through these methods, the first or last frame of the shot is taken as the key frame of the shot. The principle of this method is relatively simple, regardless of the content of the shot, the number of key frames is determined, but the effect is not stable. Since the first and last frames of each shot may not always reflect the main content of the shot, the extracted key frames are not representative enough.The method based on content analysis is to extract key frames by changing visual information such as color and texture of each frame. The classical methods are frame average method and histogram average method. Frame averaging method is to calculate the average pixel value of all frames at a certain position in the lens and then take the frame whose pixel value is closest to the average value as the key frame. Histogram averaging method is to average the statistical histogram of all frames in a shot and then select the frame closest to the histogram as the key frame. These two methods are simple to calculate, and the selected key frames have average representative significance. However, selecting a fixed number of key frames cannot describe a shot with multiple objects moving. Zhang et al. [25] proposed to extract key frames based on the image content information such as frame color and motion. The basic idea is to use the first frame as a key frame. The other key frames are then determined according to two criteria: first, the standard based on color histogram, and the subsequent frames are sequentially compared with this frame. When the distance between the first frame and the previous key frame exceeds the value, the first frame is used as the new key frame. This is done until the last frame. Second, based on the criteria of motion, for zooming scene at least the first and last frames are selected as key frames. One shows global information, the other shows local information in focus, and for panning scene, frames where the overlapping information is less than the closed value are selected as key frames. The method is more flexible, allowing the selection of a corresponding number of key frames depending on the degree of change in the content of the footage, but the algorithm only calculates distances to adjacent frames, which is prone to missed detection and is prone to redundancy when there is a lot of camera movement.This is an approach based on motion analysis. A representative algorithm for extracting key frames based on motion information is the optical flow method proposed by Nurse, which first calculates the optical flow for each frame and then calculates the amount of motion based on the optical flow. By finding the local minimum of the amount of motion, the frame in which it is located is used as the key frame. This method is based on the analytical calculation of the amount of motion in the footage and the selection of key frames at their local minima, reflecting the stillness of the video data.Specifically, first the usage calculates the optical flow by summing the modes of each pixel optical flow component as the motionM(k) for the kth frame, i.e.,(1)Mk=∑i∑jOxi,j,k+Oyi,j,k,where Ox(i, j, k) is the component X of the optical flow of pixel (i, j) in frame k and Oy(i, j, k) is the component Y of the optical flow of pixel (i, j) in frame k. The local minimum of M(k) is then found. Starting from k = 0, the M(k) and k curves are scanned to find local maxima M(k1) and M(k2), requiring the values of M(k1) and M(k2) to differ by at least N% (empirically set, N% = 30% is desirable). If M(k3) = min(M(k)), k1 < k < k2, then k3 is taken as the key frame. The method allows the selection of the appropriate number of key frames according to the structure of the shot, but it relies on local information, is not very robust, requires a large amount of computation, and is less time efficient, and the local minima in the method are not always accurate.Based on clustering method, the clustering algorithm is a very effective technique widely used in pattern recognition, speech analysis, and information retrieval. The basic idea is to start with an initialized cluster, then determine whether the current frame is classified as that class or as a new class center based on the distance between the current frame and the class center, and after classifying the frames in the shot, take the frame closest to the class center in each class as the key frame. Among the many clustering algorithms, mean clustering and fuzzy mean clustering are two well-known clustering algorithms. The classification of the mean clustering algorithm is clear, with each sample being assigned to an entry belonging to only one cluster. The classification of the fuzzy mean clustering algorithm is fuzzy, with each sample having a membership function for each cluster. These clustering methods are effective in eliminating inter-shot correlations and obtaining more desirable key frames, but do not effectively maintain the temporal order and dynamic information of the image frames within the original shot.According to the analysis of key frame extraction methods, each method has certain advantages and disadvantages. In comparison, the key frames extracted based on clustering methods are more effective, but according to the analysis of the theoretical knowledge related to fuzzy clustering, the results of clustering are usually closely related to the input parameters such as the number of clusters and the initial cluster centers. These parameters are also often difficult to decide on, especially with datasets of high-dimensional objects like images, making the quality of the clustering difficult to control. In addition, metrics using Euclidean distance are sometimes not stable enough in noisy environments, and the eye is too sensitive to the shape and size of the class.In this research, the concept of fullness in information theory is applied to the process of fuzzy clustering classification and metrics, and an improved key frame extraction method is used. The stability of the fuzzy clustering algorithm in the key frame extraction application is improved using mutual information quantity, as shown in Figure2.Figure 2
Key frame extraction method.Firstly, the shot boundary of the video sequence is detected by using the mutual information between adjacent frames, and the video fragment is divided into several sub-scenes. Then, the improved fuzzy clustering method is used to extract key frames in the shot. In the process of clustering analysis, there is no need of any user input parameters related to clustering model, but according to the density function method to determine the initial clustering center, and the use of the average office value to initialize clustering number, it is beneficial to maintain the stability of the clustering effect, and mesh using inter-frame office value will also be able to keep the time sequence of video and dynamic information. The obtained key frame can better represent the main content of the video sequence.


## 2.1. Video Hierarchy

Video is the most complex type of multimedia information. It is a comprehensive media information integrating image, sound, and text. It has the advantages of large amount of information and vivid form of expression. The video data can be structurally divided into video sequence, scene, shot, and frame from top to bottom (Figure1). A frame is the smallest unit of video data, a still picture. The lens is the basic unit of video data. It consists of several consecutive frames of images taken continuously by a camera in time. There are two types of camera switching: abrupt change and gradual change. The mutation is a direct transition from one shot to the next. There is no time delay gradient in the middle, but some editing effect in space or time is added, and the previous shot slowly transitions to the next shot. There are mainly fade in and fade out, slow conversion and sweep conversion. A scene consists of scene of similar content, depicting the same event from different angles. Video sequences consist of many scenes and generally tell a complete story.Figure 1
Hierarchical structure of video.As can be seen from Figure1, the higher the hierarchical structure of the video, the richer the content information contained therein, which means the higher the difficulty of processing. Therefore, top-down anatomical analysis is often used for video processing. Firstly, the video sequence is divided into multiple scenes by scene detection, and then each scene is divided into multiple scene by shot segmentation. Then, the key frame of each shot is extracted as the main content of the video sequence. In this process, scene detection, shot segmentation, and key frame extraction are involved because the lens is the basic unit of video data. At present, the more common method is to directly take the video clip as the unit, detect the shot first, and then extract the key frame without scene detection.


## 2.2. Extraction of Short-Form Video Key Frames

Key frame refers to the most important and representative image frame in the lens. It reflects the main content of a shot and is the basis for building a video sequence index. Using key frame technology to query, search, and browse, video database effectively can greatly reduce the amount of video data. It also provides a unified organizational framework for video processing. It also provides a unified organizational framework for video processing. Depending on the complexity of the shot content, one or more key frames can be extracted from a shot. Key frame extraction must be able to reflect the main content information in the shot more accurately. Secondly, the dynamic information of video sequence changes should be preserved to facilitate the indexing and management of video sequence. The traditional manual extraction method has high accuracy, but it is time-consuming and inefficient. At present, automatic detection technology is mainly used to extract key frames. Typical key frame extraction methods mainly include shot boundary-based method, content-based method, motion-based method, and clustering-based method. This paper proposes a key frame extraction method combining mutual information and fuzzy clustering. The method uses information entropy instead of Euclidean distance to calculate the similarity between frames and determines the number of clusters according to the average information entropy. The density function method is used to obtain the initial clustering center, which does not require users to input parameters related to clustering, so that the obtained key frames can not only better represent the main content of the lens, but also have good stability. Meanwhile, the time series and dynamic information of the video are maintained.


## 2.3. Traditional Key Frame Extraction Method

This method is based on shot boundary. In video sequences, shot boundaries are divided into two categories: shear and gradient. A shear is when a change occurs suddenly between two consecutive frames, while a gradient is when a change occurs between consecutive frames. Gradients are complex, including fading in and out, dissolving and erasing. At present, many researchers have proposed a variety of different lens boundary detection methods, such as calculating the pixel difference of the corresponding position of adjacent image frames, algorithm based on histogram difference, and method based on edge information. Through these methods, the first or last frame of the shot is taken as the key frame of the shot. The principle of this method is relatively simple, regardless of the content of the shot, the number of key frames is determined, but the effect is not stable. Since the first and last frames of each shot may not always reflect the main content of the shot, the extracted key frames are not representative enough.The method based on content analysis is to extract key frames by changing visual information such as color and texture of each frame. The classical methods are frame average method and histogram average method. Frame averaging method is to calculate the average pixel value of all frames at a certain position in the lens and then take the frame whose pixel value is closest to the average value as the key frame. Histogram averaging method is to average the statistical histogram of all frames in a shot and then select the frame closest to the histogram as the key frame. These two methods are simple to calculate, and the selected key frames have average representative significance. However, selecting a fixed number of key frames cannot describe a shot with multiple objects moving. Zhang et al. [25] proposed to extract key frames based on the image content information such as frame color and motion. The basic idea is to use the first frame as a key frame. The other key frames are then determined according to two criteria: first, the standard based on color histogram, and the subsequent frames are sequentially compared with this frame. When the distance between the first frame and the previous key frame exceeds the value, the first frame is used as the new key frame. This is done until the last frame. Second, based on the criteria of motion, for zooming scene at least the first and last frames are selected as key frames. One shows global information, the other shows local information in focus, and for panning scene, frames where the overlapping information is less than the closed value are selected as key frames. The method is more flexible, allowing the selection of a corresponding number of key frames depending on the degree of change in the content of the footage, but the algorithm only calculates distances to adjacent frames, which is prone to missed detection and is prone to redundancy when there is a lot of camera movement.This is an approach based on motion analysis. A representative algorithm for extracting key frames based on motion information is the optical flow method proposed by Nurse, which first calculates the optical flow for each frame and then calculates the amount of motion based on the optical flow. By finding the local minimum of the amount of motion, the frame in which it is located is used as the key frame. This method is based on the analytical calculation of the amount of motion in the footage and the selection of key frames at their local minima, reflecting the stillness of the video data.Specifically, first the usage calculates the optical flow by summing the modes of each pixel optical flow component as the motionM(k) for the kth frame, i.e.,(1)Mk=∑i∑jOxi,j,k+Oyi,j,k,where Ox(i, j, k) is the component X of the optical flow of pixel (i, j) in frame k and Oy(i, j, k) is the component Y of the optical flow of pixel (i, j) in frame k. The local minimum of M(k) is then found. Starting from k = 0, the M(k) and k curves are scanned to find local maxima M(k1) and M(k2), requiring the values of M(k1) and M(k2) to differ by at least N% (empirically set, N% = 30% is desirable). If M(k3) = min(M(k)), k1 < k < k2, then k3 is taken as the key frame. The method allows the selection of the appropriate number of key frames according to the structure of the shot, but it relies on local information, is not very robust, requires a large amount of computation, and is less time efficient, and the local minima in the method are not always accurate.Based on clustering method, the clustering algorithm is a very effective technique widely used in pattern recognition, speech analysis, and information retrieval. The basic idea is to start with an initialized cluster, then determine whether the current frame is classified as that class or as a new class center based on the distance between the current frame and the class center, and after classifying the frames in the shot, take the frame closest to the class center in each class as the key frame. Among the many clustering algorithms, mean clustering and fuzzy mean clustering are two well-known clustering algorithms. The classification of the mean clustering algorithm is clear, with each sample being assigned to an entry belonging to only one cluster. The classification of the fuzzy mean clustering algorithm is fuzzy, with each sample having a membership function for each cluster. These clustering methods are effective in eliminating inter-shot correlations and obtaining more desirable key frames, but do not effectively maintain the temporal order and dynamic information of the image frames within the original shot.According to the analysis of key frame extraction methods, each method has certain advantages and disadvantages. In comparison, the key frames extracted based on clustering methods are more effective, but according to the analysis of the theoretical knowledge related to fuzzy clustering, the results of clustering are usually closely related to the input parameters such as the number of clusters and the initial cluster centers. These parameters are also often difficult to decide on, especially with datasets of high-dimensional objects like images, making the quality of the clustering difficult to control. In addition, metrics using Euclidean distance are sometimes not stable enough in noisy environments, and the eye is too sensitive to the shape and size of the class.In this research, the concept of fullness in information theory is applied to the process of fuzzy clustering classification and metrics, and an improved key frame extraction method is used. The stability of the fuzzy clustering algorithm in the key frame extraction application is improved using mutual information quantity, as shown in Figure2.Figure 2
Key frame extraction method.Firstly, the shot boundary of the video sequence is detected by using the mutual information between adjacent frames, and the video fragment is divided into several sub-scenes. Then, the improved fuzzy clustering method is used to extract key frames in the shot. In the process of clustering analysis, there is no need of any user input parameters related to clustering model, but according to the density function method to determine the initial clustering center, and the use of the average office value to initialize clustering number, it is beneficial to maintain the stability of the clustering effect, and mesh using inter-frame office value will also be able to keep the time sequence of video and dynamic information. The obtained key frame can better represent the main content of the video sequence.


## 3. Motion Frame Extraction in Video

The difficulty of video moving object extraction is mainly reflected in two aspects. Due to the rich and colorful real world, the content of video scene is extremely complicated and diversified, so it is difficult to find a general method to extract moving objects from various videos. The definition of video object is a kind of high-level semantic description, which is difficult to be described by low-level visual features such as color, texture, and edge. At present, there is a “semantic gap” research bottleneck in the field of video image, which means that the current computer vision technology is far from reaching the ability of human visual recognition.Despite the difficulties, many scholars have made great achievements in the field of video segmentation. According to the different information used in the extraction process, video object segmentation algorithms can be divided into three categories: spatial-domain segmentation technology based on within frames, temporal domain segmentation technology based on between frames, and spatial and temporal information fusion. According to the degree of manual participation, it can be divided into automatic segmentation method and semi-automatic segmentation method.Based on inter-frame difference method, background subtraction method, and optical flow method, a moving object extraction method (IBO) combining spatio-temporal information and geometric curve evolution is proposed in this paper. Firstly, the high-order statistics algorithm is used to detect the motion regions of two adjacent frames in time domain, and the initial motion contour is extracted after morphological changes. Then, the improved watershed algorithm is used to segment the image into regions in space domain. Finally, the better motion object contour can be obtained by combining spatio-temporal information. Finally, the contour of the moving object is taken as the initial contour of the geometric active contour model in view of the existing void, redundant or missing image information.


### 3.1. Geometric Curve Evolution

The time-domain information segmentation technology and spatial-domain information segmentation technology, respectively, make use of the in-frame and inter-frame information of the video. For a complete video sequence, they are only part of the information and can only reflect part of the characteristics of the video scene, so there are certain limitations. The spatio-temporal joint segmentation algorithm combines the moving object identified by time-domain segmentation and the result obtained by space-domain segmentation to get the final moving object, which has more precision and effect. Figure3 shows a moving object extraction method using spatio-temporal information combined with geometric curve evolution.Figure 3
Moving object extraction process.IBO method can be divided into four steps. In the time domain, the motion information between two adjacent frames is calculated by high-order statistical method to obtain the binarized difference region, and then the morphological method is used for filtering. Finally, the initial motion contour is obtained by scanning the filled region. In spatial domain, the watershed algorithm is used to perform initial image segmentation for video frame images, and then the region fast merging method based on adjacency graph is used to reduce the oversegmentation phenomenon and get better region division effect. Combined with the comprehensive information of the previous two steps, according to the proportion value of motion information and regional information, determine the contour of the quadratic moving object. Aiming at the problems of image noise and redundant information of the quadratic moving object, the geometric active contour model combined with the spatial edge information is used to evolve the accurate moving object.


### 3.2. Frame to Frame Difference Method

In IBO method, the difference between frames is used to extract the information of moving objects. Frame difference method is a segmentation algorithm based on changing region detection. In the image sequence, the background of two adjacent frames is relatively unchanged, while the moving object changes. Frame difference method is an image segmentation method that separates the moving object from the stationary background by detecting the changing and invariant regions of the adjacent frames of the image sequence. The outstanding feature of IBO method is that it is simple to implement and fast to calculate, but inter-frame difference method is less affected by illumination changes because the ambient brightness changes between adjacent frames are very small.Assume that two consecutive frames of imageIk − 1 and Ik, and the grayscale of their pixel points are, respectively, represented by Gk− 1(x, y) and Gk(x, y); then, the frame difference image of these two frames can be expressed as follows:(2)Dkx,y=Gkx,y−Gk−1x,y.Binarize the obtained frame difference imageDk(x, y):(3)Rxx,y=255,Dkx,y>T,0,others,where T is the threshold value. In binary difference images, a pixel with a gray value of 255 is considered as a point on a moving object.Simple thresholding method can roughly separate moving target and background, but it requires presetting min value and poor flexibility, and it is difficult to filter noise interference, so it needs the next processing to separate moving target. If the grayscale difference between consecutive frames is nonzero, there may be two reasons for moving target changes and background noise. Under the condition of video pause, background noise mainly includes random noise, brightness change, slow change of background texture, etc. These noises have smaller amplitude compared with the gray value of non-noise image reflected by the target being photographed.In addition, the random process distribution of thermal noise, photoelectronic noise, and photosensitive particle noise is a stationary random process with ergodicity in theory, so the statistics of these noises all conform to Gaussian characteristics, and the moving target has a strong structure. Therefore, the problem of separating moving object and background can be transformed into the problem of separating non-Gaussian data from Gaussian data.Set the small window of 3 × 3 centered on (x, y) in frame difference graph Dk(x, y); then, the fourth moment md4 (x, y) and second moment md2 (x, y) of point (x, y) are defined as follows:(4)mdnx,y=13×3∑s,t∈ηx,yDks,t−mdx,yn,where η(x, y) represents the 3 × 3 field centered on the current pixel and md is the average value of the differential gray image between frames in the window.(5)mdx,y=13×3∑s,t∈ηx,yDks,t.In general, direct estimation of the fourth-order cumulants of random distribution variables is tedious, so the relationship between the fourth-order cumulants and the fourth-order momentsmd4 (x, y) and two moments md2 (x, y) can be used to solve the problem.(6)HOS4x,y=0,|HOS4x,y≤TH,1,others,where TH is the set threshold, as shown in the following formula.(7)TH=c×1N∑i=1NHOS4,where N is the number of pixels in the whole image and c is the scale factor.


### 3.3. Region Fast Merging Algorithm

Watershed segmentation algorithm has a wide range of applications and good segmentation results when the SNR and consistency of images are satisfied. However, due to the existence of image noise and the local irregularity of gradient, the image after watershed segmentation is easy to produce excessive; that is, the segmentation is too detailed. In this paper, a similar region merging algorithm is used to deal with over-scraping phenomenon. The algorithm is based on the assumption that each pixel is an independent image region or belongs to a segmented region, and the region is adjusted and merged by the similarity degree of the histogram of the region, so that the similarity degree between the regions is kept at a certain distance.Makeμi, σi (i = 1, 2, …, m), respectively, represent the mean and standard deviation of pixels in the region, and Ni is the number of pixels in the region. Then, the standard for the combination of two adjacent regions based on statistics is as follows:(8)μ1−μ2≤α,(9)S=μ1−μ2σp21/n1+1/n2≤β,where α and β are the setting parameters given in advance by the statistical characteristics of the image. If σp2=0, any two regions satisfying equation (8) are considered similar; if σp2=0, the two regions satisfying equation (9) are considered similar.If there is more than one similar adjacency region, the most similar adjacency region is merged. Then, a new region is selected from the remaining regions that did not participate in the merging until all similar adjacent regions in the image are merged.However, due to the large number of regions obtained by initial watershed segmentation, the common similar region merging algorithm is limited in speed due to the large amount of computation in the process of seeking the optimal merging region through iterative operation. In order to improve the speed of region merging, a region merging algorithm based on adjacency graph is adopted in this paper on the basis of image gradient preprocessing to avoid iterative operation and corresponding data update and reduce the complexity of region merging. Each connection in a region adjacency graph has two states, which determine the adjacency relationship between a region and the current processing region. The image region adjacency graph is stored in a table that is updated with the region adjacency relationship during region merging. The update process is shown in Figure4.Figure 4
Adjacency diagram before and after merging.As can be seen from Figure4, regions A and B are relatively similar. A∗ is the new region of A and B, so the adjacent region will be changed accordingly.Curve evolution is an effective method for image segmentation and video object tracking. The active contour algorithm was proposed in the early stage, but the model itself has some defects, such as sensitivity to the initial position, easy to fall into local extremum, and cannot automatically carry out topological transformation. Although people have made some improvements to the basic active contour model, it has not fundamentally solved the problem. A geometric active contour model based on level set, also called curve evolution model, is based on the theory of curve evolution and the idea of level set. Its principle is to express the plane closed curve implicitly as the level set of three-dimensional surface function, that is, the set of points with the same function value, and then solve the curve evolution implicitly through the evolution of the surface. The most important characteristic of this model is that it does not depend on the parameterization mode of active contour model, so it can deal with the change of topological structure of curve naturally. However, these characteristics are inseparable from level set theory. Geometric active contour model and level set method complement each other.


## 3.1. Geometric Curve Evolution

The time-domain information segmentation technology and spatial-domain information segmentation technology, respectively, make use of the in-frame and inter-frame information of the video. For a complete video sequence, they are only part of the information and can only reflect part of the characteristics of the video scene, so there are certain limitations. The spatio-temporal joint segmentation algorithm combines the moving object identified by time-domain segmentation and the result obtained by space-domain segmentation to get the final moving object, which has more precision and effect. Figure3 shows a moving object extraction method using spatio-temporal information combined with geometric curve evolution.Figure 3
Moving object extraction process.IBO method can be divided into four steps. In the time domain, the motion information between two adjacent frames is calculated by high-order statistical method to obtain the binarized difference region, and then the morphological method is used for filtering. Finally, the initial motion contour is obtained by scanning the filled region. In spatial domain, the watershed algorithm is used to perform initial image segmentation for video frame images, and then the region fast merging method based on adjacency graph is used to reduce the oversegmentation phenomenon and get better region division effect. Combined with the comprehensive information of the previous two steps, according to the proportion value of motion information and regional information, determine the contour of the quadratic moving object. Aiming at the problems of image noise and redundant information of the quadratic moving object, the geometric active contour model combined with the spatial edge information is used to evolve the accurate moving object.


## 3.2. Frame to Frame Difference Method

In IBO method, the difference between frames is used to extract the information of moving objects. Frame difference method is a segmentation algorithm based on changing region detection. In the image sequence, the background of two adjacent frames is relatively unchanged, while the moving object changes. Frame difference method is an image segmentation method that separates the moving object from the stationary background by detecting the changing and invariant regions of the adjacent frames of the image sequence. The outstanding feature of IBO method is that it is simple to implement and fast to calculate, but inter-frame difference method is less affected by illumination changes because the ambient brightness changes between adjacent frames are very small.Assume that two consecutive frames of imageIk − 1 and Ik, and the grayscale of their pixel points are, respectively, represented by Gk− 1(x, y) and Gk(x, y); then, the frame difference image of these two frames can be expressed as follows:(2)Dkx,y=Gkx,y−Gk−1x,y.Binarize the obtained frame difference imageDk(x, y):(3)Rxx,y=255,Dkx,y>T,0,others,where T is the threshold value. In binary difference images, a pixel with a gray value of 255 is considered as a point on a moving object.Simple thresholding method can roughly separate moving target and background, but it requires presetting min value and poor flexibility, and it is difficult to filter noise interference, so it needs the next processing to separate moving target. If the grayscale difference between consecutive frames is nonzero, there may be two reasons for moving target changes and background noise. Under the condition of video pause, background noise mainly includes random noise, brightness change, slow change of background texture, etc. These noises have smaller amplitude compared with the gray value of non-noise image reflected by the target being photographed.In addition, the random process distribution of thermal noise, photoelectronic noise, and photosensitive particle noise is a stationary random process with ergodicity in theory, so the statistics of these noises all conform to Gaussian characteristics, and the moving target has a strong structure. Therefore, the problem of separating moving object and background can be transformed into the problem of separating non-Gaussian data from Gaussian data.Set the small window of 3 × 3 centered on (x, y) in frame difference graph Dk(x, y); then, the fourth moment md4 (x, y) and second moment md2 (x, y) of point (x, y) are defined as follows:(4)mdnx,y=13×3∑s,t∈ηx,yDks,t−mdx,yn,where η(x, y) represents the 3 × 3 field centered on the current pixel and md is the average value of the differential gray image between frames in the window.(5)mdx,y=13×3∑s,t∈ηx,yDks,t.In general, direct estimation of the fourth-order cumulants of random distribution variables is tedious, so the relationship between the fourth-order cumulants and the fourth-order momentsmd4 (x, y) and two moments md2 (x, y) can be used to solve the problem.(6)HOS4x,y=0,|HOS4x,y≤TH,1,others,where TH is the set threshold, as shown in the following formula.(7)TH=c×1N∑i=1NHOS4,where N is the number of pixels in the whole image and c is the scale factor.


## 3.3. Region Fast Merging Algorithm

Watershed segmentation algorithm has a wide range of applications and good segmentation results when the SNR and consistency of images are satisfied. However, due to the existence of image noise and the local irregularity of gradient, the image after watershed segmentation is easy to produce excessive; that is, the segmentation is too detailed. In this paper, a similar region merging algorithm is used to deal with over-scraping phenomenon. The algorithm is based on the assumption that each pixel is an independent image region or belongs to a segmented region, and the region is adjusted and merged by the similarity degree of the histogram of the region, so that the similarity degree between the regions is kept at a certain distance.Makeμi, σi (i = 1, 2, …, m), respectively, represent the mean and standard deviation of pixels in the region, and Ni is the number of pixels in the region. Then, the standard for the combination of two adjacent regions based on statistics is as follows:(8)μ1−μ2≤α,(9)S=μ1−μ2σp21/n1+1/n2≤β,where α and β are the setting parameters given in advance by the statistical characteristics of the image. If σp2=0, any two regions satisfying equation (8) are considered similar; if σp2=0, the two regions satisfying equation (9) are considered similar.If there is more than one similar adjacency region, the most similar adjacency region is merged. Then, a new region is selected from the remaining regions that did not participate in the merging until all similar adjacent regions in the image are merged.However, due to the large number of regions obtained by initial watershed segmentation, the common similar region merging algorithm is limited in speed due to the large amount of computation in the process of seeking the optimal merging region through iterative operation. In order to improve the speed of region merging, a region merging algorithm based on adjacency graph is adopted in this paper on the basis of image gradient preprocessing to avoid iterative operation and corresponding data update and reduce the complexity of region merging. Each connection in a region adjacency graph has two states, which determine the adjacency relationship between a region and the current processing region. The image region adjacency graph is stored in a table that is updated with the region adjacency relationship during region merging. The update process is shown in Figure4.Figure 4
Adjacency diagram before and after merging.As can be seen from Figure4, regions A and B are relatively similar. A∗ is the new region of A and B, so the adjacent region will be changed accordingly.Curve evolution is an effective method for image segmentation and video object tracking. The active contour algorithm was proposed in the early stage, but the model itself has some defects, such as sensitivity to the initial position, easy to fall into local extremum, and cannot automatically carry out topological transformation. Although people have made some improvements to the basic active contour model, it has not fundamentally solved the problem. A geometric active contour model based on level set, also called curve evolution model, is based on the theory of curve evolution and the idea of level set. Its principle is to express the plane closed curve implicitly as the level set of three-dimensional surface function, that is, the set of points with the same function value, and then solve the curve evolution implicitly through the evolution of the surface. The most important characteristic of this model is that it does not depend on the parameterization mode of active contour model, so it can deal with the change of topological structure of curve naturally. However, these characteristics are inseparable from level set theory. Geometric active contour model and level set method complement each other.


## 4. System Construction

A portal-based content-based key frame extraction system is constructed using VC6.0 platform, and the system structure is shown in Figure5.Figure 5
Key frame extraction subsystem structure diagram.As can be seen from Figure5, the system is mainly composed of five parts: video decoding, shot detection, key frame extraction, video playing, and shot playing, among which the first three modules are video core processing modules. Video playback and shot broadcast are the auxiliary functions of the system, mainly for the convenience of users to browse and view when using the system. The former is responsible for playing the whole video sequence, while the latter mainly plays and browses for a certain shot segment or the shot selected according to the key frame. In the core module, the video decoding first decodes the newly added MPEG-4 (compression coding standard) compressed video stream, then segments the whole video sequence to get the video shot set, and then extracts the key frames of each shot to get the main content of this shot.The moving object extraction method using spatio-temporal information combined with geometric curve evolution adopted above builds a moving object extraction subsystem through the platform, and its system structure is shown in Figure6.Figure 6
Structure diagram of moving object extraction subsystem.As can be seen from Figure6, the subsystem of moving object extraction is mainly composed of contour extraction, watershed segmentation, and geometric curve evolution. Contour extraction means that in the time domain of video sequence, the frame difference image between two adjacent frames is calculated by high-order statistics, and then the binarization moving contour is obtained by morphological processing and connectivity scanning filling, which is mapped to the original image frame to obtain the initial moving object. The purpose of watershed segmentation is to divide the image into multiple regions according to the spatial information of the image frame and solve the oversegmentation phenomenon by using the region fast merging algorithm based on adjacency graph. Combining with the initial moving object, the image cable ratio method is used to determine the cubic moving object. The main function of geometric curve evolution is to eliminate the noise by level set method in view of the image noise and cavity of the secondary moving object, so as to obtain the final moving object.According to the demand of video material retrieval, a prototype subsystem of video material retrieval is constructed in this paper. After extracting key frames and moving objects, the video material is stored in the server image library in the format of pictures, and the video information description and the color, shape, texture, contour, and other characteristic information of key frames and moving objects are saved in the material database. Its system structure is shown in Figure7.Figure 7
Video material retrieval subsystem structure diagram.Keyword query: semantic automatic extraction has “semantic gap” problem. The solution is to obtain the initial description set of keywords by manual annotation, and then train and modify keywords by using relevant feedback technology so as to achieve the purpose of automatic query of keywords.Main tone query: color is the most significant feature of color image, and users can easily remember the color features of any object and give one or several main tones of the image. Color is the basic element of an image, such as blue, by describing an image with the sea or sky. In the same way, color is important to describe an animated image. Generally, bright colors are used to match similar things, and the corresponding things will be matched when querying the color.Sketch query: hand-drawn sketch is an externalization and communication of a common human way of thinking. Sketches express and convey the concept of visual space, and image, intuitive, easy to understand and remember, and more suitable for reasoning and conception. Local objects in a sketch without a background grid tend to be clearer than in an image.Example query: sample sources include the system to randomly give samples and users to submit samples. In the former, a group of image training samples is randomly given by the system, and users are allowed to evaluate the group of images and select images similar to their retrieval requirements. Then, the images selected by users are analyzed, and other similar images are retrieved through the calculation of information lineal between images. In the latter, users upload a similar image that they are interested in, and the system analyzes and retrieves relevant materials.


## 5. Conclusion

In this paper, the key frame technology of short video extraction is used to effectively control the behavior of minors participating in online short video. Based on the existing key frame extraction technology, a new key frame extraction technology (IBO technology) is proposed. In the method of key frame extraction, the initial cluster center is determined by the density function method and the number of clusters is determined by average information entropy. This method does not require users to input any parameters related to the clustering pattern, and can automatically complete the clustering process and maintain the stability of the clustering effect. This method can effectively extract key frames of minors’ participation in online short video and help the government to effectively monitor online short video platforms.



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*Source: 8985219-2022-06-02.xml*