By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can in simple terms be simply accessed via quick shopping and retrieval concepts. developing a video desk of contents (ToC) and video highlights to let finish clients to sift via all this knowledge and locate what they need, 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 built a cohesive option 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 speak about the iteration of desk of contents, extraction of highlights, various innovations for audio and video marker reputation, and indexing with low-level good points reminiscent of colour, texture, and form. present functions together with this summarization and skimming expertise also are reviewed. functions akin 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 most recent in learn and readers will locate their look for wisdom pleased through the breadth of the knowledge coated during this quantity. * deals the newest in leading edge examine and purposes in surveillance and buyer video* Presentation of a unique unified framework aimed toward effectively sifting in the course of the abundance of photos collected day-by-day at procuring department shops, airports, and different advertisement amenities* Concisely written through major members 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
4 AUDIO MARKER DETECTION Audio markers are key audio classes that indicate the events of interest in unscripted content. In our previous work on sports, audience reaction and commentator’s excited speech are classes that have been shown to be useful markers [26, 27]. Nepal et al.  detect basketball “goal” based on crowd cheers from the audio signal using energy thresholds. Another example of an audio marker, consisting of key words such as “touchdown” or “fumble,” has been reported . 5 VIDEO MARKER DETECTION Visual markers are key video objects that indicate the events of interest in unscripted content.
It better models the “temporal locality” without having the “window effect,” and it constructs more accurate scene structures by taking comprehensive information into account. It has four major modules: shot boundary detection and key frame extraction, spatiotemporal feature extraction, time-adaptive grouping, and scene structure construction. While features can include both visual and audio features , this chapter focuses on the visual feature. The same framework can be applied to audio and textual features.
While shot is the building block of a video, it is scene that conveys the semantic meaning of the video to the viewers. The discontinuity of shots is overwhelmed by the continuity of a scene . Video ToC construction at the scene level is thus of fundamental importance to video browsing and retrieval. In Bolle et al. , a scene transition graph (STG) of video representation is proposed and constructed. The video sequence is ﬁrst segmented into shots. Shots are then clustered by using time-constrained clustering.