By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can basically be simply accessed via swift looking and retrieval ideas. developing a video desk of contents (ToC) and video highlights to permit 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 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 buyer and surveillance video functions. The authors speak about the new release of desk of contents, extraction of highlights, varied suggestions for audio and video marker attractiveness, and indexing with low-level good points reminiscent of colour, texture, and form. present functions together with this summarization and perusing expertise also are reviewed. functions comparable 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 e-book offers the most recent in study and readers will locate their look for wisdom completely happy via the breadth of the knowledge lined during this quantity. * deals the most recent in innovative examine and functions in surveillance and client video* Presentation of a singular unified framework geared toward effectively sifting during the abundance of photos accumulated day-by-day at procuring shops, airports, and different advertisement amenities* Concisely written by means of prime individuals 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
To reﬂect the relative importance of each feature, different weights are then associated with the features. The relative importance of a feature can be estimated from the statistics of its feature array As . For example, if all the elements in As are of 32 2. Video Table-of-Content Generation similar value, then this particular feature is of little discriminating power and should receive low weight. On the other hand, if the elements in As demonstrate variation, then the feature has good discriminating power and should receive high weight.
Because of video’s length and its unstructured format, efﬁcient access to video is not an easy task. 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.
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.
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