By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can simply be simply accessed via speedy shopping and retrieval recommendations. 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, once they wish are crucial. This reference places forth a unified framework to combine those capabilities helping effective searching 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 purposes in purchaser and surveillance video purposes. The authors talk about the iteration of desk of contents, extraction of highlights, assorted concepts for audio and video marker reputation, and indexing with low-level good points comparable to colour, texture, and form. present purposes together with this summarization and perusing know-how also are reviewed. purposes comparable to 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 offers the newest in learn and readers will locate their look for wisdom joyful by means of the breadth of the knowledge lined during this quantity. * deals the newest in leading edge learn and functions in surveillance and client video* Presentation of a unique unified framework geared toward effectively sifting in the course of the abundance of pictures accrued day-by-day at buying department stores, airports, and different advertisement amenities* Concisely written by way of top participants within the sign processing with step by step guideline in development video ToC and indices
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Additional resources for A Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance Video
2 Audio markers for sports highlights extraction. 3 Examples of visual markers for different sports. soccer, we want to detect the appearance of the goalpost. Correct detection of these key visual objects can eliminate the majority of the video content that is not in the vicinity of the interesting segments. For the goal of one general framework for all three sports, we use the following processing strategy: for the unknown sports content, we detect whether there are baseball catchers or golfers bending to hit the ball, or soccer goalposts.
Procedure: (1) Denote the current shot as shot i and the scene having the largest similarity to shot i as scene smax . That is, shot i belongs to scene smax . , current shot). 25) merge scene s into scene smax . That is, if a scene contains a shot that is interlaced with the current scene, merge the two scenes. (4) Return. What distinguishes the proposed approach from the plain clusteringbased approach is the intelligence involved. The intelligence manifests itself in the algorithm in three aspects.
We also review some of the limitations of these technologies. Chapter 8 recapitulates our coverage of video browsing and indexing techniques and discusses the state of the art. We lay out the challenges for future research and development. 1 Introduction We have seen a rapid increase of the usage of multimedia information. Of all the media types (text, image, graphic, audio, and video), video is the most challenging, as it combines all other media information into a single data stream. Owing to the decreasing cost of storage devices, higher transmission rates, and improved compression techniques, digital video is becoming available at an ever-increasing rate.