By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can merely be simply accessed by way of quick searching and retrieval concepts. developing a video desk of contents (ToC) and video highlights to let 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 services aiding effective looking and retrieval of video content material. The authors have constructed a cohesive technique to create a video desk of contents, video highlights, and video indices that serve to streamline using purposes in buyer and surveillance video purposes.
The authors speak about the new release of desk of contents, extraction of highlights, various suggestions for audio and video marker reputation, and indexing with low-level gains akin to colour, texture, and form. present purposes together with this summarization and perusing know-how also are reviewed. functions comparable 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 provides the newest in examine and readers will locate their look for wisdom happy by means of the breadth of the knowledge lined during this quantity.
* deals the newest in leading edge study and functions in surveillance and client video
* Presentation of a unique unified framework geared toward effectively sifting throughout the abundance of pictures accumulated day-by-day at purchasing shops, airports, and different advertisement facilities
* Concisely written by means of prime members within the sign processing with step by step guideline in construction 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
However, there we have not used the MDL criterion to select the model structures, so we have not used the "optimal" models. Now equipped with the MDL-GMMs and with the observation that they can greatly improve classification accuracy, we revisit the problem in Xiong et al. . 2, we train the MDL-GMMs on all the data in the ground truth set. To gain a better understanding of classification, especially on the applause/ cheering sound, we also test all the data in the ground truth set before we test the game data.
Also, we observe that the complexity of music is between that of applause or cheering and speech. For speech with music (a mixture class of speech and music), its complexity is between the two classes that are in the mixture. 2 EVALUATION USING THE PRECISION-RECALL CURVE Using the length L of contiguous applause or cheering as a parameter, we can count the number of true highlights A^ in the C candidates that have applause or cheering length greater than or equal to L. , Pr = ^ ) . 4 MDL(K, ^)(Y-axis) with respect to different numbers of GMM mixtures ^(X-axis) to model (a) applause, (b) cheering, (c) music, (d) speech, and (e) speech with music sound shown in the raster-scan order.
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.
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