Download A Unified Framework for Video Summarization, Browsing & by Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong PDF

By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang

Huge volumes of video content material can simply be simply accessed by means of swift searching and retrieval concepts. developing a video desk of contents (ToC) and video highlights to permit finish clients to sift via all this knowledge and locate what they wish, after they wish are crucial. This reference places forth a unified framework to combine those capabilities helping effective looking and retrieval of video content material. The authors have constructed a cohesive method to create a video desk of contents, video highlights, and video indices that serve to streamline using functions in shopper and surveillance video functions. The authors speak about the new release of desk of contents, extraction of highlights, varied strategies for audio and video marker popularity, and indexing with low-level good points akin to colour, texture, and form. present functions together with this summarization and skimming expertise also are reviewed. functions resembling occasion detection in elevator surveillance, spotlight extraction from activities video, and photograph 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 joyful via the breadth of the data coated during this quantity. * bargains the newest in innovative study and purposes in surveillance and purchaser video* Presentation of a singular unified framework aimed toward effectively sifting in the course of the abundance of photos accrued day-by-day at procuring shops, airports, and different advertisement amenities* Concisely written by means of top participants within the sign processing with step by step guide in construction video ToC and indices

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Additional resources for A Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance Video

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3 PLAY/BREAK SEGMENTATION Since unscripted content has short periods of activity (plays) between periods of inactivity (breaks), it is useful to first segment the content into these units. This helps to reduce the amount of content to be analyzed for subsequent processing that looks for highlight segments within plays. Play/break segmentation for sports, both in an unsupervised and supervised manner using low-level features, has been reported [24]. Play/break segmentation in surveillance has been reported using adaptive background subtraction techniques that identify periods of object activity from the whole content [25].

5] divide the whole video stream into multiple video segments, each of which contains an equal number of consecutive shots. Each segment is further divided into subsegments, thus constructing a hierarchy of video content that is used to assist browsing. In this approach, time is the only factor considered, and no visual content is used in constructing the browsing hierarchy. In contrast, Zhong et al. [3] proposed a cluster-based video hierarchy in which the shots are clustered based on their visual content.

Update 0 1 2 3 4 0 0 0 1 1 0 0 0 0 0 0 0 . . 3 Merging scene 1 to scene 0. ● Procedure: (1) Denote the current shot as shot i and the scene having the largest similarity to shot i as scene smax . That is, shot i belongs to scene smax . , current shot). 25) merge scene s into scene smax . That is, if a scene contains a shot that is interlaced with the current scene, merge the two scenes. (4) Return. What distinguishes the proposed approach from the plain clusteringbased approach is the intelligence involved.

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