By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can purely be simply accessed via speedy shopping and retrieval strategies. developing a video desk of contents (ToC) and video highlights to permit finish clients to sift via all this knowledge and locate what they need, after they wish are crucial. This reference places forth a unified framework to combine those features assisting effective searching and retrieval of video content material. The authors have constructed a cohesive solution to create a video desk of contents, video highlights, and video indices that serve to streamline using purposes in shopper and surveillance video purposes. The authors speak about the iteration of desk of contents, extraction of highlights, diverse recommendations for audio and video marker attractiveness, and indexing with low-level gains resembling colour, texture, and form. present purposes together with this summarization and skimming expertise also are reviewed. functions 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 booklet offers the newest in study and readers will locate their look for wisdom happy via the breadth of the knowledge coated during this quantity. * deals the newest in innovative learn and functions in surveillance and buyer video* Presentation of a singular unified framework geared toward effectively sifting in the course of the abundance of photos amassed day-by-day at procuring shops, airports, and different advertisement amenities* Concisely written through prime participants within the sign processing with step by step guide in construction video ToC and indices
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Extra resources for A Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance Video
Take the shot and key frame–based video ToC, for example. It is not uncommon for a modern movie to contain a few thousand shots and key frames. This is evident in Yeung et al. —there are 300 shots in a 15-minute video segment of the movie Terminator 2: Judgment Day, and the movie lasts 139 minutes. Because of the large number of key frames, a simple one-dimensional (1D) sequential presentation of key frames for the underlying video is almost meaningless. More important, people watch the video by its semantic scenes, not the physical shots or key frames.
35] propose a time-constrained clustering approach to grouping shots, where the similarity between two shots is set to 0 if their time difference is greater than a predeﬁned threshold. We propose a more general time-adaptive grouping approach based on the two properties for 22 2. Video Table-of-Content Generation the similar shots just described. In our proposed approach, the similarity of two shots is an increasing function of visual similarity and a decreasing function of frame difference. Let i and j be the indices for the two shots whose similarity is to be determined, where shot j > shot i.
In the model-based approach, an a priori model of a particular application or domain is ﬁrst constructed. This model speciﬁes the scene boundary characteristics, based on which the unstructured video stream can be abstracted into a structured representation. The theoretical framework of this approach has been proposed by Swangberg, Shu, and Jain , and it has been successfully realized in many interesting applications, including news video parsing  and TV soccer program parsing . Since this approach is based on speciﬁc application models, it normally achieves high accuracy.