Key Frame Extraction Using Unsupervised Clustering Based on a Statistical Model

Recognizing Human action in video is an very active research topic. There are a growing variety of human action datasets with different video length, different practitioners. Make human action recognition becomes a very difficult topic. A majority researchers solve the problem by extracting key frames from the videos. Most paper use feature Clustering methods to extract key frames in videos. On one hand, the large variety of visual content in videos make handcraft feature isn't effective enough, since there are no fixed descriptors can describe all video cases. On the other hand, traditional clustering algorithms are easily influenced by the choice of initial clustering centers. An Unsupervised feature learning and clustering method for key frame extraction is proposed in this paper, which can be used for human action recognition. Stacked auto-encoder(SAE) is trained using videos from 10 different human actions, SAE is used as a feature extractor to learn features representing human actions. Affinity Propagation Clustering algorithm is used to select key frames from video sequences. We use a variety of videos to do the experiments. Experiments demonstrate that our method can be effectively summarizing video shots considering different human actions.

Key frame extraction is one of the critical techniques in computer vision fields such as video search, video identification and video forgeries detection. The extracted key frames should be sufficient key frames that preserve main actions in a video with compact representation. The objective of this work is to improve our previous action key frames extraction algorithm (AKF) by adapting a threshold which selects action key frames as final key frames. The threshold adaptation was achieved by using the mean value, the standard deviation, and the L1-norm instead of the comparison of user summaries evaluation method to obtain a fully automatic video summarisation algorithm, and by eliminating the conditions in selecting the final key frames to reduce the complexity of the algorithm. We have validated our proposed Improved AKF on complex colour video shots instead of the simple grey level video shots. The Improved AKF algorithm was able to extract a compact number of action key frames by preventing redundant key frames, reduce processing complexity, and preserve sufficient information about the main actions in a video shot. We then evaluated the Improved AKF algorithm with the-state-of-the-art algorithms in terms of compression ratio using Paul videos and Shih-Tang dataset. The evaluation results showed that the Improved AKF algorithm achieved better compression ratio and retained sufficient information in the extracted action key frames under different testing video shots. Therefore, the improved AKF algorithm is a suitable technique for applications in computer vision fields such as passive object-based video authentication systems

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.

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.

Key frame extraction is an important manner of video summarization. It can be used to interpret video content quickly. Existing approaches first partition the entire video into video clips by shot boundary detection, and then, extract key frames by frame clustering. However, in most team-sport videos, a video clip usually includes many events, and it is difficult to extract the key frames related to all of these events accurately, because different events of a game shot can have features of similar appearance. As is well known, most events in team-sport videos are attack and defense conversions, which are related to global translation. Therefore, by using fine-grained partition based on the global motion, a shot could be further partitioned into more video clips, from which more key frames could be extracted and they are related to the events. In this study, global horizontal motion is introduced to further partition video clips into fine-grained video clips. Furthermore, global motion statistics are utilized to extract candidate key frames. Finally, the representative key frames are extracted based on the spatial–temporal consistence and hierarchical clustering, and the redundant frames are removed. A dataset called SportKF is built, which includes 25 videos of 197,878 frames in 112 min and 764 key frames from four types of sports (basketball, football, American football and field hockey). The experimental results demonstrate that the proposed scheme achieves state-of-the-art performance by introducing global motion statistics.

The process of key frames extraction is facing human subjectivity. The objectives of key frames extraction are mainly two objectives. The first objective is minimizing the number of extracted key frames. The second objective is maximizing the visual representation of a video shot that the key frames should have. There is a tradeoff between the two objectives. Also the needs of individuals are varied. Some individuals favor the compression ratio and others favor the representation level. Many works depend on a subjective ground truth of the key frames for assessing the performance of key frames extraction methods. In this paper we propose a key frame extraction system that takes a confidence level as input from the user to satisfy the different needs. We evaluate our system using fair measures which are the compression ratio and the fidelity that are directly related to the confidence level.

For the existing traditional method to extract key frames from the video sequence with gradual change of content, there are problems of having too much redundancy or too much loss in the extraction results and other issues, a new key frames extracting method for adaptively extracting the video key frames has proposed in the paper. The experimental results have shown that the proposed method can effectively extract video key frames, especially for the video with gradual change of content, which can express more succinctly and comprehensively. The experiments on a large number of videos containing various scenes have shown that the key frames extracted by the proposed algorithm can more fully express the main content of video. When dealing with content gradient video, the effect is better. The content adaptively determines the number of key frames based on video.

In this paper, we present a new solution for extracting key frames from motion capture data using an optimization algorithm to obtain compact and sparse key frame data that can represent the original dense human body motion capture animation. The use of the genetic algorithm helps determine the optimal solution with global exploration capability while the use of a probabilistic simplex method helps expedite the speed of convergence. By finding the chromosome that maximizes the fitness function, the algorithm provides the optimal number of key frames as well as the low reconstruction error with an ordinary interpolation technique. The reconstruction error is computed between the original motion and the reconstruction one by the weighted differences of joint positions and velocities. The resulting set of key frames is obtained by iterative application of the algorithm with initial populations generated randomly and intelligently. We also present experiments which demonstrate that the method can effectively extract key frames with a high compression ratio and reconstruct all other non key frames with high quality.

As the fast development of network and multimedia technology, there are mass of videos on the Internet. The problem of how to browse the large volume of video data is urgent to be solved. Key frame extraction is a very important technology in online video retrieval and video abstraction. Efficient key frame extraction can boost the development of video browsing technology. In this paper, we first review some commonly-used key frame extraction technology, and then propose a improved key frame extraction algorithm, which measures the similarity of two adjacent frames' contents in terms of the information of frame difference and extracts key frames after optimizing the frame difference. The experiment results show that the proposed algorithm can extract key information from online video efficiently, and has a good description in the aspect of motion of the camera lens.

Key Frame Extraction Techniques: A Survey

Nowadays, a strong need exists for the efficient organization of an increasing amount of home video content. To create an efficient system for the management of home video content, it is required to categorize home video content in a semantic way. So far, a significant amount of research has already been dedicated to semantic video categorization. However, conventional categorization approaches often rely on unnecessary concepts and complicated algorithms that are not suited in the context of home video categorization. To overcome the aforementioned problem, this paper proposes a novel home video categorization method that adopts semantic home photo categorization. To use home photo categorization in the context of home video, we segment video content into shots and extract key frames that represent each shot. To extract the semantics from key frames, we divide each key frame into ten local regions and extract lowlevel features. Based on the low level features extracted for each local region, we can predict the semantics of a particular key frame. To verify the usefulness of the proposed home video categorization method, experiments were performed with home video sequences, labeled by concepts part of the MPEG-7 VCE2 dataset. To verify the usefulness of the proposed home video categorization method, experiments were performed with 70 home video sequences. For the home video sequences used, the proposed system produced a recall of 77% and an accuracy of 78%.

With the improvement of people's security awareness, numerous monitoring equipment has been put into use, resulting in the explosive growth of surveillance video data. Key frame extraction technology is a paramount technology for improving video storage efficiency and enhancing the accuracy of video retrieval. It can extract key frame sets that can express video content from massive videos. However, the existing key frame extraction algorithms of surveillance video still have deficiencies, such as the destruction of image information integrity and the inability to extract key frames accurately. To this end, this paper proposes a key frame extraction algorithm of surveillance video based on quaternion Fourier saliency detection. Firstly, the algorithm used colors, and intensity features to perform quaternion Fourier transform on surveillance video sequences. Next, the phase spectrum of the quaternion Fourier transformed image was obtained, and he image visual saliency map was obtained according to the quaternion Fourier phase spectrum. Then, the image visual saliency map of two adjacent frames is used to characterize the change of target motion state. Finally, the frames that can accurately express the motion state of the target are selected as key frames. The experimental results show that the method proposed in this paper can accurately capture the changes of the local motion state of the target while maintaining the integrity of the image information.

With the development of network multimedia, quick and low-cost management of the massive video information is urgently needed. Key frames can decrease data quantity in video indexing and provide a framework for video retrieval and indexing. A key frame extraction method based on high-curvature points detection was proposed. First, three descriptors of color histogram, edge direction histogram and wavelet statistics were used to describe visual content, and combined to form a frame difference measure. Then the key frames were got by detecting high curvature points. Experimental results show that the proposed algorithm is rapid and efficient. It can capture precise changes dynamically in the content of video sequences, and can extract the key frames on the fly. Finally, a fidelity evaluation proves the effectiveness of the algorithm.

For the traditional method to extract the surveillance video key frame, there are problems of redundant information, substandard representative content and other issues. A key frame extraction method based on motion target detection and image similarity is proposed in this paper. This method first uses the ViBe algorithm fusing the inter-frame difference method to divide the original video into several segments containing the moving object. Then, the global similarity of the video frame is obtained by using the peak signal to noise ratio, the local similarity is obtained through the SURF feature point, and the comprehensive similarity of the video image is obtained by weighted fusion of them. Finally, the key frames are extracted from the critical video sequence by adaptive selection threshold. The experimental results show that the method can effectively extract the video key frame, reduce the redundant information of the video data, and express the main content of the video concisely. Moreover, the complexity of the algorithm is not high, so it is suitable for the key frame extraction of the surveillance video.

Rapid increase in video databases has forced the industry to have efficient and effective frameworks for video retrieval and indexing. Video segmentation into scenes is widely used for video summarization, partitioning, indexing and retrieval. In this paper, we propose a framework for scene detection mainly based on entropy and Speeded Up Robust Features (SURF) features. First, we detect the fade and abrupt boundaries based on frame entropy analysis and SURF features matching. Fade boundaries are smart indication of scenes beginning or ending in many videos and dramas, and are detected by frame entropy analysis. Before abrupt boundary detection, unnecessary frames which are obviously not abrupt boundaries, such as blank screens, high intensity influenced images, sliding credits, are removed. Candidate boundaries are detected to make SURF features efficient for abrupt boundary detection, and SURF features between candidate boundaries and their adjacent frames are used to detect the abrupt boundaries. Second, key frames are extracted from abrupt shots. We evaluate our key frame extraction with other famous algorithms and show the effectiveness of the key frames. Finally, scene boundaries are detected using sliding window of size K over the key frames in temporal order. In experimental evaluation on the TRECVID-2007 shot boundary test set, the algorithm for shot boundary achieves substantial improvements over state-of-the-art methods with the precision of 99% and the recall of 97.8%. Experimental results for video segmentation into scenes are also promising, compared to famous state-of-the-art techniques.