Download A Unified Framework for Video Summarization, Browsing and 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 purely be simply accessed by means of speedy looking and retrieval ideas. developing a video desk of contents (ToC) and video highlights to let finish clients to sift via all this knowledge and locate what they wish, once they wish are crucial. This reference places forth a unified framework to combine those features helping effective shopping and retrieval of video content material. The authors have constructed a cohesive strategy to create a video desk of contents, video highlights, and video indices that serve to streamline using functions in patron and surveillance video purposes.

The authors talk about the new release of desk of contents, extraction of highlights, various concepts for audio and video marker acceptance, and indexing with low-level gains comparable to colour, texture, and form. present functions together with this summarization and perusing know-how also are reviewed. functions reminiscent of occasion detection in elevator surveillance, spotlight extraction from activities video, and snapshot and video database administration are thought of in the proposed framework. This booklet offers the newest in study and readers will locate their look for wisdom happy by means of the breadth of the knowledge coated during this quantity.

* bargains the most recent in innovative examine and purposes in surveillance and customer video

* Presentation of a singular unified framework geared toward effectively sifting during the abundance of photos accumulated day-by-day at procuring department stores, airports, and different advertisement facilities

* Concisely written by way of top members 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

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In this application, L = K{ 1+M+- (M + 1)M\ --—-1 Notice that this criterion has a penalty term on the total number of data values NM, what Rissanen [42] called the MDL estimator. Let us denote the parameter learning of GMMs using the MDL criterion MDL-GMM. While the expectation maximization (EM) algorithm can be used to update the parameter 6, it does not solve the problem of how to change the model order K. Our approach will be to start with a large number of clusters and then sequentially decrement the value of K.

Obviously, the three sets of shots will form three groups. Suppose the first two groups have already been formed and the current shot is a shot in group 3. While the current shot may not be similar to either of groups 1 and 2, it is similar to some extent to both groups 1 and 2; and all the three groups are semantically related. This situation occurs often in video shooting. The approaches in Yeung et al. [35] and Aoki et al. [38] will not be effective in handling this, since both of them only compare the current shot to the individual groups but not to the scene as a whole.

First, the "temporal attraction" is an intelligent way of modeling the temporal factor of the similarity. Second, updateGroupScene intelligently merges related groups to a single scene. Finally, updateScene intelligently updates related scenes into a unified one. The procedure updateGroupScene and updateScene are of significant importance to the proposed scene structure construction algorithm. While findGroupSim helps to group similar shots into a group and findSceneSim helps to merge a shot (or a single-element group) into a scene, it is updateGroupScene and updateScene that link semantically related shots into a 30 2.

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