The adaptive key frames extraction method based on representativeness and independence features

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.

3D video, which consists of a sequence of mesh models, can reproduce dynamic scenes containing 3D information. To summarize 3D video, a key frame extraction method is developed using rate-distortion (R-D) trade-off. For this purpose, an effective feature vector is extracted for each frame. Shot detection is performed using the feature vectors as a preprocessing followed by key frame extraction. Simple but reasonable definitions of rate and distortion are presented. Based on an assumption of linearity, an R-D curve is generated in each shot, where the locations of the key frames are optimized. Finally, R-D trade-off can be achieved by optimizing a cost function using a Lagrange multiplier, where the number of key frames is optimized in each shot. Therefore, our system will automatically determine the best locations and the number of key frames in the sense of R-D trade-off. Our experimental results show the extracted key frames are compact and faithful to the original 3D video.

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.

In the current era, most of the digital information is in the form of multimedia with a giant share of videos. Videos do have audio and visual content where the visual content has number of frames put in a sequence. Most of the consecutive frames do have very little discriminative contents. In video summarization process, several frames containing similar information do need to get processed. This leads to redundant slow processing speed and complexity, time consumption. Video summarization using key frames can ease the speed up of video processing. In this paper, novel key frames extraction method is proposed with Kekere's Proportionate Error (KPE) codebook generation techniques of vector quantization with ten different codebook sizes and two color spaces (RGB and KLUV). Experimentation done with help of the test bed of videos has shown that higher codebook sizes of KPE have given better completeness in key frame extraction for video summarization. The LUV color space with Euclidean Distance with 512 codebook size gives best performance. In square chord Distance, Mean Square Error and Euclidean Distance LUV color space gives better completeness than RGB color space for proposed KPE based video Key frame Extraction

Key Frame Extraction is a key step for video content analysis and video retrieval. According to the motion of object and camera, a method of key frame extraction based on frame blocks differential accumulation with double thresholds is proposed. Firstly, image frames are partitioned into equal-sized sub-images. Then the blocks in current frames are compared with the corresponding blocks in last extracted key frame to detect weather their contents changed. At last, the key frames of the video can be determined by the number of blocks whose contents changed a lot. The experiment results show that this method has a satisfactory efficiency.

The purpose of video key frame extraction is to use as few video frames as possible to represent as much video content as possible, reduce redundant video frames, and reduce the amount of computation, so as to facilitate quick browsing, content summarization, indexing, and retrieval of videos. In this paper, a method of dance motion recognition and video key frame extraction based on multifeature fusion is designed to learn the complicated and changeable dancer motion recognition. Firstly, multiple features are fused, and then the similarity is measured. Then, the video sequences are clustered by the clustering algorithm according to the scene. Finally, the key frames are extracted according to the minimum amount of motion. Through the quantitative analysis and research of the simulation results of different models, it can be seen that the model proposed in this paper can show high performance and stability. The breakthrough of video clip retrieval technology is bound to effectively promote the inheritance and development of dance, which is of great theoretical significance and practical value.

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.

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.

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

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.

Key frames play a very important role in video indexing and retrieval. In this paper, we propose a novel method to extract key frames based on information theory. We use improved Bayesian Information Criterion to determining the number of key frames, and then automatically extract key frames to represent video shot based on information bottleneck cluster method. Experimental results and the comparisons with other methods on various types of video sequences show the effectiveness of the proposed method.

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.

In order to solve the problem of action recognition in short video and capture the key information of video, this paper first proposes a KGAF-means method for key frame extraction. The KGAF-means method is based on the clustering principle and combines the K-means algorithm with the artificial fish swarm algorithm to realize the key frame sequence extraction. Based on the extracted key frame sequence, the RGB image and the optical flow image are separately extracted by the improved dual-stream variable convolution network. Then, using the cascading method, the image feature vector and the optical flow feature vector are fused to obtain the fused feature vector for action recognition. The selected data set is the Charades data set. The experimental results show that the mAP value of the method is 22.9 on the public dataset Charades. And the results show that the proposed method has better robustness than other network models and improves the short video action recognition effect.

We present an automatic and efficient method to register and stitch thousands of video frames into a large panoramic mosaic. Our method preserves the robustness and accuracy of image stitchers that match all pairs of images while utilizing the ordering information provided by video. We reduce the cost of searching for matches between video frames by adaptively identifying key frames based on the amount of image-to-image overlap. Key frames are matched to all other key frames, but intermediate video frames are only matched to temporally neighboring key frames and intermediate frames. Image orientations can be estimated from this sparse set of matches in time quadratic to cubic in the number of key frames but only linear in the number of intermediate frames. Additionally, the matches between pairs of images are compressed by replacing measurements within small windows in the image with a single representative measurement. We show that this approach substantially reduces the time required to estimate the image orientations with minimal loss of accuracy. Finally, we demonstrate both the efficiency and quality of our results by registering several long video sequences

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.