In this paper, we present VSCAN, a novel approach for generating static video summaries. This approach is based on a modified DBSCAN clustering algorithm to summarize the video content utilizing both color and texture features of the video frames. The paper also introduces an enhanced evaluation method that depends on color and texture features. Video Summaries generated by VSCAN are compared with summaries generated by other approaches found in the literature and those created by users. Experimental results indicate that the video summaries generated by VSCAN have a higher quality than those generated by other approaches.
In @cite_9 , an approach called VSUMM (Video SUMMarization) is presented. In the first step, the video frames are pre-sampled by selecting one frame per second. In the second step, the color features of video frames are extracted from Hue component only in the HSV color space. In the third step, the meaningless frames are eliminated. In the fourth step, the frames are clustered using k-means algorithm where the number of clusters is estimated by computing the pairwise Euclidean distances between video frames and a key frame is extracted from each cluster. Finally, another extra step occurs in which the key frames are compared among themselves through color histogram to eliminate those similar key frames in the produced summaries.
@cite_9
1987366351 2144139464 2028788183 2016543560
The fast evolution of digital video has brought many new multimedia applications and, as a consequence, has increased the amount of research into new technologies that aim at improving the effectiveness and efficiency of video acquisition, archiving, cataloging and indexing, as well as increasing the usability of stored videos. Among possible research areas, video summarization is an important topic that potentially enables faster browsing of large video collections and also more efficient content indexing and access. Essentially, this research area consists of automatically generating a short summary of a video, which can either be a static summary or a dynamic summary. In this paper, we present VSUMM, a methodology for the production of static video summaries. The method is based on color feature extraction from video frames and k-means clustering algorithm. As an additional contribution, we also develop a novel approach for the evaluation of video static summaries. In this evaluation methodology, video summaries are manually created by users. Then, several user-created summaries are compared both to our approach and also to a number of different techniques in the literature. Experimental results show - with a confidence level of 98 - that the proposed solution provided static video summaries with superior quality relative to the approaches to which it was compared. The authors propose a novel technique for video summarization based on singular value decomposition (SVD). For the input video sequence, we create a feature-frame matrix A, and perform the SVD on it. From this SVD, we are able, to not only derive the refined feature space to better cluster visually similar frames, but also define a metric to measure the amount of visual content contained in each frame cluster using its degree of visual changes. Then, in the refined feature space, we find the most static frame cluster, define it as the content unit, and use the context value computed from it as the threshold to cluster the rest of the frames. Based on this clustering result, either the optimal set of keyframes, or a summarized motion video with the user specified time length can be generated to support different user requirements for video browsing and content overview. Our approach ensures that the summarized video representation contains little redundancy, and gives equal attention to the same amount of contents. In this paper, we propose a video summarization algorithm by multiple extractions of key frames in each shot. This algorithm is based on the k-medoid clustering algorithms to find the best representative frame for each video shot. This algorithm, which is applicable to all types of descriptors, consists of extracting key frames by similarity clustering according to the given index. In our proposal, the distance between frames is calculated using a fast full search block matching algorithm based on the frequency domain. The proposed approach is computationally tractable and robust with respect to sudden changes in mean intensity within a shot. Additionally, this approach produces different key frames even in the presence of large motion. The experiments results show that our algorithm extracts multiple representatives frames in each video shot without visual redundancy, and thus it is an effective tool for video indexing and retrieval. With the development of imaging techniques and the Internet, it is hard to effectively and efficiently manage, index and store large amounts of videos. Video summarization appears to this need by obtaining useful information in videos. Recently, the reconstruction concept has been introduced into video summarization. However, most of these existing reconstruction methods where a dictionary of key frames is selected to reconstruct the original video best might come up with redundant information. In this paper, we propose a novel framework named Video Summarization based on Nonnegative Linear Reconstruction (VSNLR) which allows only additive, not subtractive, linear combinations. Our approach consists of two core steps: (1) we detect the shot boundary and choose a representative frame for every shot, then all the representative frames form the candidate set; (2) for every frame in the candidate set, VSNLR selects related frames to reconstruct the given frame by using the nonnegative linear reconstruction function. The key frames are selected by minimizing the sum of reconstruction errors. Experiments on a dataset and comparison to the state-of-art method demonstrate our advantage.