By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can merely be simply accessed by means of speedy looking and retrieval suggestions. developing a video desk of contents (ToC) and video highlights to allow finish clients to sift via all this information and locate what they wish, once they wish are crucial. This reference places forth a unified framework to combine those capabilities assisting 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 purposes in patron and surveillance video purposes. The authors speak about the iteration of desk of contents, extraction of highlights, diversified thoughts for audio and video marker reputation, and indexing with low-level beneficial properties corresponding to colour, texture, and form. present functions together with this summarization and perusing expertise also are reviewed. purposes equivalent to occasion detection in elevator surveillance, spotlight extraction from activities video, and photograph and video database administration are thought of in the proposed framework. This publication provides the newest in study and readers will locate their look for wisdom joyful via the breadth of the knowledge lined during this quantity. * deals the most recent in innovative study and functions in surveillance and purchaser video* Presentation of a singular unified framework geared toward effectively sifting throughout the abundance of pictures accumulated day-by-day at procuring shops, airports, and different advertisement amenities* Concisely written through major participants within the sign processing with step by step guideline in construction video ToC and indices
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Additional info for A Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance Video
The ground truth of the scene boundaries for the tested video sequences are obtained from subjective tests. Multiple human subjects are invited to watch the movies and then are asked to give their own scene structures. The structure that most people agree with is used as the ground truth of the experiments. 1: ● ● ● The proposed scene construction approach achieves reasonably good results in most of the movie types. The approach achieves better performance in the “slow” movies than in the “fast” movies.
Different videos may require different thresholds. But after the Gaussian normalization procedure, the similarity distribution of any feature for any video is normalized to the Gaussian N(0, 1) distribution, making the determination of thresholds much easier. 4. 4 The Gaussian N (0, 1) distribution. 5 Experimental Results 33 A learning process can be designed to ﬁnd appropriate values for the two thresholds. Since any feature’s similarity in any video is mapped to the same distribution, the training feature and video can be arbitrary.
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A Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance Video by Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang