By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can simply be simply accessed by way of speedy looking and retrieval thoughts. developing a video desk of contents (ToC) and video highlights to let finish clients to sift via all this knowledge and locate what they wish, after they wish are crucial. This reference places forth a unified framework to combine those capabilities assisting effective searching and retrieval of video content material. The authors have constructed a cohesive approach to create a video desk of contents, video highlights, and video indices that serve to streamline using purposes in purchaser and surveillance video purposes. The authors talk about the iteration of desk of contents, extraction of highlights, diverse recommendations for audio and video marker reputation, and indexing with low-level positive aspects resembling colour, texture, and form. present functions together with this summarization and perusing know-how also are reviewed. functions reminiscent of occasion detection in elevator surveillance, spotlight extraction from activities video, and picture and video database administration are thought of in the proposed framework. This publication provides the newest in examine and readers will locate their look for wisdom blissful by way of the breadth of the knowledge coated during this quantity. * deals the most recent in leading edge study and purposes in surveillance and client video* Presentation of a singular unified framework aimed toward effectively sifting throughout the abundance of photos collected day-by-day at buying shops, airports, and different advertisement amenities* Concisely written via best participants within the sign processing with step by step guideline in development 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
Another approach to scene-based video ToC construction does not require such an explicit domain model. Three of the pioneering works of this approach are from Institute de Recherche en Informatique de Toulouse (IRIT), France , Princeton University, and IBM [2, 11, 32, 35] and Toshiba Corp. . 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.
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. 4. During the scene construction procedure, μC , σC , μA , and σA are used to convert the raw similarity values to normalized ones. 3) where WC and WA are appropriate weights for color and activity measures; and ShotColorSimi,j and ShotActSimi,j are the raw similarity values. This procedure converts the raw similarities into similarities that obey the normal distribution of N(0, 1).
Similarly, that “there is a soccer goal attempt” is represented by the detection of the soccer goalpost followed by the detection of long and loud audience cheering. Representation based on highlight groups supports more detailed 12 1. Introduction queries than the previous representation. 1, the ToC, highlights, and index are equally important for accessing the video content. Unlike the other video representations, the mosaic representation is especially suitable for video retrieval. Three components—moving objects, backgrounds, and camera motions— are perfect candidates for a video index.