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 by way of swift looking and retrieval options. developing a video desk of contents (ToC) and video highlights to permit finish clients to sift via all this information and locate what they need, once they wish are crucial. This reference places forth a unified framework to combine those services aiding effective shopping and retrieval of video content material. The authors have built a cohesive strategy to create a video desk of contents, video highlights, and video indices that serve to streamline using functions in shopper and surveillance video purposes. The authors speak about the iteration of desk of contents, extraction of highlights, diverse options for audio and video marker reputation, and indexing with low-level beneficial properties resembling colour, texture, and form. present functions together with this summarization and skimming expertise also are reviewed. functions resembling 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 provides the most recent in learn and readers will locate their look for wisdom completely happy by means of the breadth of the data coated during this quantity. * bargains the newest in leading edge examine and functions in surveillance and purchaser video* Presentation of a singular unified framework aimed toward effectively sifting in the course of the abundance of photos amassed day-by-day at purchasing shops, airports, and different advertisement amenities* Concisely written by means of prime individuals within the sign processing with step by step guideline in development 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
Fortunately, video is not the ﬁrst long medium. Access to a book is greatly facilitated by a well-designed table of contents (ToC) that captures the semantic structure of the book. For today’s video, the lack of such a ToC makes the task of browsing and retrieval inefﬁcient, because a user searching for a particular object of interest has to use the time-consuming fast-forward and rewind functions. , DVD) has incorporated the concept of chapters into the video metadata. But that has to be done manually and is not practical for a large archive of digital video.
2) Denote the current shot as shot i. (a) Compute the similarity in terms of F between shot i and shot i , i = i − MULTIPLE, · · · , i − 1. Note that only the similarities of the previous MULTIPLE shots need to be calculated, since shots outside MULTIPLE have zero temporal attraction to the current shot. 4 Determination of the Parameters 31 (b) Store the calculated similarity values in an array As . (c) Goto Step 1. (3) Let NA be the number of entries in array As . Consider this array as a sequence of Gaussian variables, and compute the mean μAs and standard deviation σAs of the sequence.
Aigrain, Joly, and Longueville  propose a multimodal rule-based approach. They ﬁrst identify local (in time) rules, which are given by the medium contents, and then construct scenes (which they call “macrosegments”) by combining the rules. In  and , the video stream is ﬁrst segmented into shots. Then a time-constrained clustering is used to construct visually similar and temporally adjacent shots into clusters. Finally, a scene transition graph (STG) is constructed based on the clusters, and cutting edges are identiﬁed to construct the scene structure.
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