Key Frame Extraction Algorithm Research Articles

Model of network video retrieval of dynamic Bayesian network optimization for the mobile terminal

ABSTRACT Key-frame extraction is a key technique of video retrieval. In view of the low accuracy of currently traditional key-frame extraction algorithm, and low recall level and accuracy of video retrieval, a kind of model of network video retrieval of dynamic Bayesian network optimization for the mobile terminal was put forward. First of all, the study of dynamic Bayesian network optimization algorithm was conducted, and by using a Bayesian algorithm, the real data were used as the characteristic of the center of clustering, then the condensed coarsening and the condensed refining that are two important processes for network video retrieval model algorithm were improved, used to replace the top core set obtained in the original coarsening process, thereby designing a kind of new dynamic Bayesian network optimization algorithm, so as to realize the rapid and accurate positioning of the top core set, which can decrease the number of condensed layer and simplify the computational complexity of the algorithm. Then, the improved network video retrieval model algorithm was applied to the key-frame extraction of the video. The experimental results show that the proposed algorithm can more effectively extract key frames of video compared with the original algorithm.

The comparison and analysis of extracting video key frame

Video key frame extraction is an important part of the large data processing. Based on the previous work in key frame extraction, we summarized four important key frame extraction algorithms, and these methods are largely developed by comparing the differences between each of two frames. If the difference exceeds a threshold value, take the corresponding frame as two different keyframes. After the research, the key frame extraction based on the amount of mutual trust is proposed, the introduction of information entropy, by selecting the appropriate threshold values into the initial class, and finally take a similar mean mutual information as a candidate key frame. On this paper, several algorithms is used to extract the key frame of tunnel traffic videos. Then, with the analysis to the experimental results and comparisons between the pros and cons of these algorithms, the basis of practical applications is well provided.

A Clustering Algorithm for Key Frame Extraction Based on Density Peak

Aiming at the problem of video key frame extraction, a density peak clustering algorithm is proposed, which uses the HSV histogram to transform high-dimensional abstract video image data into quantifiable low-dimensional data, and reduces the computational complexity while capturing image features. On this basis, the density peak clustering algorithm is used to cluster these low-dimensional data and find the cluster centers. Combining the clustering results, the final key frames are obtained. A large number of key frame extraction experiments for different types of videos show that the algorithm can extract different number of key frames by combining video content, overcome the shortcoming of traditional key frame extraction algorithm which can only extract a fixed number of key frames, and the extracted key frames can represent the main content of video accurately.

基于压缩感知与SURF特征的手语关键帧提取算法

基于压缩感知与SURF特征的手语关键帧提取算法

An improved video key-frame extraction algorithm leads to video watermarking

The epidemic growth of internet services has created a broad attention on digital content authentication and copyright protection. Heading the issue we are going to propose a DCT based novel approach on invisible video watermarking along with an improved video key-frame extraction algorithm. In this article first the key-frames (video bookmarks) of the video sequence are being extracted. In order to identify the video bookmarks, a novel method is proposed based on boundary luminosity analysis that is proven efficient regarding fast camera movement and moving objects in the video frames. A pair of secret keys will be applied during the watermark embedding phase. Our framework embeds watermarks in only those video frames which have been identified as key-frames of that video stream because, the key-frames are mostly kept as it is in different compression algorithms. The low frequency DCT coefficients of key-frames are chosen for watermarking, that again offers robustness to the compression algorithms. Instead of embedding watermark bits directly, a new method is proposed- scaled average. The watermark is perceptually invisible in the video key-frames according to human visual system. At last uncompressed video is re-reconstructed with all frames of the video in appropriate order of appearance. The watermark extraction is blind i.e., except the keys nothing is needed for watermark extraction.

A Meaningful Compact Key Frames Extraction in Complex Video Shots

Key frame extraction is an essential technique in the computer vision field. The extracted key frames should brief the salient events with an excellent feasibility, great efficiency, and with a high-level of robustness. Thus, it is not an easy problem to solve because it is attributed to many visual features. This paper intends to solve this problem by investigating the relationship between these features detection and the accuracy of key frames extraction techniques using TRIZ. An improved algorithm for key frame extraction was then proposed based on an accumulative optical flow with a self-adaptive threshold (AOF_ST) as recommended in TRIZ inventive principles. Several video shots including original and forgery videos with complex conditions are used to verify the experimental results. The comparison of our results with the-state-of-the-art algorithms results showed that the proposed extraction algorithm can accurately brief the videos and generated a meaningful compact count number of key frames. On top of that, our proposed algorithm achieves 124.4 and 31.4 for best and worst case in KTH dataset extracted key frames in terms of compression rate, while the the-state-of-the-art algorithms achieved 8.90 in the best case.

Key frame extraction scheme based on sliding window and features

With the rapid development of the Internet and P2P technology, multimedia resources are gradually adding and used widely. Since network traffic increases sharply, how to choose the interested information for a number of Internet users is challenging. So, technologies and applications, such as video search, video fast browsing, video index and storage are in great demand. Behind these technologies and applications, an important problem is how to quickly browse massive video data and obtain the main content of the video. To solve this problem, different key frame extraction algorithms have been proposed. Due to the diversity of video content, different video have different characteristics. So the design of general video key frame extraction algorithm to solve the problem is not the reality. The main trend for the problem is to design the key frame extraction algorithm based on the characteristics of the video itself. In this article, we mainly focus on videos with edited boundaries and shot conversions. Aiming at this kind of video, we have designed and implemented video key frame extraction algorithm based on sliding window, the global feature Gist and local feature point detection algorithm SURF. In this algorithm, we use Gist feature to construct the global scene information of frames,and the SURF key point detection algorithm to extract local key points as local feature for each frame. Then, shot segmentation based on sliding window and shot merging algorithm is applied to dividing the original video into several shots. After that,we select the most representative frames in each video shot as key frames. Finally we evaluate the result of the algorithm from the subjective and objective perspective. Results show that key frames extracted in the algorithm are of high quality and can basically cover the main content of the original video.

Pornographic video detection with MapReduce

In the big data era, the video data of social media increase rapidly. To detect and block pornographic videos, traditional pornographic image detection methods cannot be applied directly to large-scaled video data. For this purpose, a parallel computing network has been set up by a lot of cheap computers for massive pornographic video data detection. First, we propose a key-frame extraction algorithm based on inter-frame similarity. This algorithm only uses the local information of the video and can be dispatched to multiple computers for parallel processing. The results of key-frame extraction are persisted to the distributed file system. Next, in order to determine whether a video contains pornographic key-frames, we propose a discriminative multiple Gaussian mixture models to extract skin color regions and an active relevance feedback bootstrap algorithm to detect the face. Finally, the geometric characteristics of the body are used to determine whether the key-frame is a pornographic image, and according to the number of pornographic key-frame in the video to decide whether the video is pornographic or not. Compared with some existing methods, the detection accuracy has been greatly improved. Because of the proposed methods are processed in different computer nodes for parallel computing, the processing speed is only related to the scale of the video data and the number of the computers. In practical applications, it can meet demands only need to select enough computers according to the scale of the video data. In theory, it can be used for video data at any scale.

A robust and secure key-frames based video watermarking system using chaotic encryption

Currently we are facing a wide interest in multimedia security and copyright protection due to the explosion of data exchange in the Internet and the extensive use of digital media. In this paper, we propose a video watermarking method in which watermark information are encrypted using a new chaotic encryption, and then, embedded in the key-frames extracted from the video stream. Under this framework, a simple and fast key-frames extraction algorithm based on gradient magnitude similarity deviation (GMSD) is used. This algorithm can significantly decrease the complexity of video watermarking systems. In order to insert the watermark in a blind manner, new insertion and extraction functions are designed by means of a quantization process. A double transformation domain based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is adopted to robustly embed the watermark with low visual distortion. Evaluation study is conducted to verify the performance through a series of experiments. The proposed system outperforms several recent algorithms found in the literature in terms of the robustness and imperceptibility under potential attacks. Furthermore, the security requirement of the proposed algorithm is achieved with the proposed chaotic encryption procedure.

A New Algorithm for Key Frame Xtraction Based on Depth Map Using Kinect

In this paper, a new algorithm for key frame extraction based on depth map for hand gesture recognition is presented. The all input sequences are captured by Microsoft Kinect camera system. These methods extract three key frames from captured depth video sequence. These key frames describe dynamic gesture. The proposed extraction method is composed of two parts. The first part, labelled as space segmentation extracts the region of hand from background. The second part labelled as time segmentation splits captured sequence into three parts and marks one frame per part as the key frame. A new gesture database for evaluation of proposed method was created. The proposed method to human evaluators was compared. The experimental results show that the proposed system obtained accuracy about 90%.

Video Partitioning and Secured Keyframe Extraction of MPEG Video

Video partitioning and keyframe extraction (KFE) are the key foundations of video analysis and Content based video retrieval. The use of keyframes reduces the amount of data that is necessary in video indexing and provides the outline for dealing with the video content. In the last few years, many algorithms of keyframe extraction concentrated on the original compressed video stream. This can increase computational complexity when decompression is required before video processing. Keyframe is the frame, which can be a prototype of the salient content and information of the shot. The keyframes extracted must summarize the significant features of the video in time sequence. Therefore, there is a commensurate need of an efficient and secured keyframe selection technique in an efficient CBVR system. We propose an algorithm for keyframe extraction of compressed video shots using adaptive threshold method. Extensive computation on 200 plus video clips is performed and results are accurate and satisfactory.

A Key Frame Extraction Algorithm Based on Clustering and Compressive Sensing

A Key Frame Extraction Algorithm Based on Clustering and Compressive Sensing

The Key Frame Extraction Algorithm Based on the Indigenous Disturbance Variation Difference Video

The Key Frame Extraction Algorithm Based on the Indigenous Disturbance Variation Difference Video

RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis.

Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content. Several applications, such as video summarization, search, indexing, and prints from video, can benefit from extracted key frames of the video under consideration. Most approaches in this class of algorithms work directly with the input video data set, without considering the underlying low-rank structure of the data set. Other algorithms exploit the low-rank component only, ignoring the other key information in the video. In this paper, a novel key frame extraction framework based on robust principal component analysis (RPCA) is proposed. Furthermore, we target the challenging application of extracting key frames from unstructured consumer videos. The proposed framework is motivated by the observation that the RPCA decomposes an input data into: 1) a low-rank component that reveals the systematic information across the elements of the data set and 2) a set of sparse components each of which containing distinct information about each element in the same data set. The two information types are combined into a single l1-norm-based non-convex optimization problem to extract the desired number of key frames. Moreover, we develop a novel iterative algorithm to solve this optimization problem. The proposed RPCA-based framework does not require shot(s) detection, segmentation, or semantic understanding of the underlying video. Finally, experiments are performed on a variety of consumer and other types of videos. A comparison of the results obtained by our method with the ground truth and with related state-of-the-art algorithms clearly illustrates the viability of the proposed RPCA-based framework.

Human motion capture data retrieval based on semantic thumbnail

We present a method for the efficient retrieval and browsing of immense amounts of realistic 3D human body motion capture data. The proposed method organizes motion capture data based on statistical K-means (SK---means), democratic decision making, unsupervised learning, and visual key frame extraction, thus achieving intuitive retrieval by browsing thumbnails of semantic key frames. We apply three steps for the efficient retrieval of motion capture data. The first is obtaining the basic type clusters by clustering motion capture data using the novel SK-means algorithm, and after which, immediately performing character matching. The second is learning the retrieval information of users during the retrieval process and updating the successful retrieval rate of each data; the search results are then ranked on the basis of successful retrieval rate by democratic decision making to improve accuracy. The last step is generating thumbnails with semantic generalization, which is conducted by using a novel key frame extraction algorithm based on visualized data analysis. The experiment demonstrates that this method can be utilised for the efficient organization and retrieval of enormous motion capture data.

Motion Key-Frames Extraction Based on Locally Linear Embedding

A motion key-frames extraction algorithm based on LLE was presented. First, the dimensionality of motion capture data was reduced by LLE method to obtain the low dimension feature curve. Second, the initial key-frames were obtained by extracting the local extreme points. In the end, according to the difference of feature curve amplitude and the threshold, the corresponding key-frames were inserted to get the final key-frames. A amount of experiments showed that the algorithm could generalize the content of original motion sequence on the basis of the compression ratio and low error rate.

High-speed visualization of time-varying data in large-scale structural dynamic analyses with a GPU

Large-scale structural dynamic analyses generally produce massive amount of time-varying data. Inefficient rendering of these data seriously affects the quality of display of and user interaction with the analysis results. A high-speed visualization solution using a GPU (graphics processing unit) is thus developed in this study. Based on the clustering concept, a key frame extraction algorithm specific to the GPU-based rendering is proposed, which significantly reduces the data size to satisfy the GPU memory requirement. Using the key frames, a GPU-based parallel frame interpolation algorithm is also proposed to reconstruct the complete structural dynamic process. Particularly, a novel data access model considering the features of time-varying data and GPU memory is designed to improve the interpolation efficiency. Two case studies including an arch bridge and a high-rise building are presented, confirming the ability of the proposed solution to provide a high-speed and interactive visualization environment for large-scale structural dynamic analyses.

A Network Erotic Video Detection Method Based on NN-SVM and Decision Fusion

Network erotic video detection is difficult due to the complexity of their content. The key of detection is the extraction of video characteristics and the construction of classifier. In this article we proposed a network erotic video detection method based on NN-SVM and decision fusion. First, network data stream was resolved to be video data, which would be sampled to be key frame sets. An adaptive key frame extraction algorithm was designed for the huge video data flow of the high-speed network. Second, frame static characteristics extraction algorithm was designed to train the NN-SVM classifier, and video dynamic feature was obtained to reflect the relationship of frame sequence. Third, the final classification results were achieved by the fusion of the recognition results of the NN-SVM classifier and video dynamic feature. At the same time, small class models were designed to rapidly identify specific categories of videos, which would be likely to identify by mistake. The comparative experiments showed that our method for erotic video detection was quite accurate and efficient.

Hierarchical key frame extraction for wireless capsule endoscopy video based on the saliency map

Wireless capsule endoscopy (WCE) is a non-invasive imaging technique to examine the entire small intestine. This paper presents a novel hierarchical key frame extraction algorithm based on the saliency map to automatically select a small number of key informative frames in the WCE video to assist the diagnostic process. We start from calculating the saliency map and extracting the inside colour, texture and shape features. We then compute the image information entropies with adapted weights on three features; calculate the differences of entropies in the successive frames and extract the first level key frames to divide the video into multi-segments. Finally, the mean shift clustering method is applied on each segment to obtain the final key frames. The proposed technique achieves the fidelity of 95.07% and the compression ratio of 87.9% on average, validating that the proposed scheme is highly promising for the key frame extraction in the WCE video.

Video key frame extraction method based on image dominant color

Video key frame reflects the main content of the video sequence. Video key frame extraction is one of the key steps for video content retrieval. Although there are some effective key frame extraction algorithms, these algorithms still have some problems such as heavy load of computing, difficulty in choosing suitable threshold value for different type sequences and limited types of videos. In this paper, a video key frame extraction method based on frame dominant color was proposed.Firstly, every frame was simplified by the dominant color which was obtained by octree structure color quantization algorithm.Secondly, shot boundary was detected according to the color similarity between adjacent frames. Finally, key frames were decided from candidate frames by K-means clustering algorithm. The experimental results show that the proposed method is simpler in computation and requires lower time and space complexity than other key frame extraction methods.