By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can merely be simply accessed via quick looking and retrieval strategies. developing a video desk of contents (ToC) and video highlights to permit finish clients to sift via all this information and locate what they need, after they wish are crucial. This reference places forth a unified framework to combine those features aiding effective shopping and retrieval of video content material. The authors have built a cohesive approach to create a video desk of contents, video highlights, and video indices that serve to streamline using functions in shopper and surveillance video purposes.
The authors speak about the iteration of desk of contents, extraction of highlights, diversified thoughts for audio and video marker attractiveness, and indexing with low-level gains corresponding to colour, texture, and form. present functions together with this summarization and skimming know-how also are reviewed. functions similar to occasion detection in elevator surveillance, spotlight extraction from activities video, and photo and video database administration are thought of in the proposed framework. This ebook offers the newest in examine and readers will locate their look for wisdom glad by means of the breadth of the data lined during this quantity.
* bargains the newest in leading edge examine and purposes in surveillance and buyer video
* Presentation of a singular unified framework aimed toward effectively sifting during the abundance of pictures collected day-by-day at purchasing shops, airports, and different advertisement facilities
* Concisely written via prime participants within the sign processing with step by step guide in development video ToC and indices
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Extra info for A Unified Framework for Video Summarization, Browsing and Retrieval. With Applications to Consumer and Surveillance Video
Video is a sequential medium. Therefore, even though two or more processes are developing simultaneously in a video, they have to be displayed sequentially, one after another. This is conunon in a movie. For example, when two people are talking to each other, even though both people contribute to the conversation, the movie switches back and forth between these two people. In this example, clearly two groups exist, one corresponding to person A and the other corresponding to person B. Even though these two groups are nonsimilar, they are semantically related and constitute a single scene.
For any algorithm to be of practical use, all the parameters should be determined either automatically by the algorithm itself or easily by the user. In our proposed algorithm, Gaussian normalization is used in determining the four parameters. Specifically, Wc, and WA are determined automatically by the algorithm, and groupThreshold and sceneThreshold are determined by user's interaction. 19), we combine color histogram similarity and activity similarity to form the overall shot similarity. Since the color histogram feature and activity feature are from two totally different physical domains, it would be meaningless to combine them without normalizing them first.
38], a search window that is eight shots long is used when calculating the shot similarities. While this "window" approach is a big advance from the plain unsupervised clustering in video analysis, it has the problem of discontinuity ("window effects"). For example, if the frame difference between two shots is T — 1, then the similarity between these two shots is kept unchanged. But if these two shots are a bit farther apart from each other, making the frame difference to be T + 1, the similarity between these shots is suddenly cleared to 0.
A Unified Framework for Video Summarization, Browsing and Retrieval. With Applications to Consumer and Surveillance Video by Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang