Content Coverage and Redundancy Removal in Video Summarization

2017 ◽  
pp. 352-374
Author(s):  
Hrishikesh Bhaumik ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Video Retrieval ◽  
Video Summarization ◽  
Viewing Time ◽  
Redundancy Removal ◽  
Retrieval Systems ◽  
Media Types ◽  
Content Coverage ◽  
Video Retrieval Systems ◽  
Audio Video

Over the past decade, research in the field of Content-Based Video Retrieval Systems (CBVRS) has attracted much attention as it encompasses processing of all the other media types i.e. text, image and audio. Video summarization is one of the most important applications as it potentially enables efficient and faster browsing of large video collections. A concise version of the video is often required due to constraints in viewing time, storage, communication bandwidth as well as power. Thus, the task of video summarization is to effectively extract the most important portions of the video, without sacrificing the semantic information in it. The results of video summarization can be used in many CBVRS applications like semantic indexing, video surveillance copied video detection etc. However, the quality of the summarization task depends on two basic aspects: content coverage and redundancy removal. These two aspects are both important and contradictory to each other. This chapter aims to provide an insight into the state-of-the-art approaches used for this booming field of research.

scholarly journals Human Face Detection in Color Images

2021 ◽  
Author(s):  
Jun Gao
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Video Retrieval ◽  
Color Images ◽  
Human Face ◽  
Detection Scheme ◽  
Retrieval Systems ◽  
Human Face Detection ◽  
The Face ◽  
Video Retrieval Systems

Detection of human face has many realistic and important applications such as human and computer interface, face recognition, face image database management, security access control systems and content-based indexing video retrieval systems. In this report a face detection scheme will be presented. The scheme is designed to operate on color images. In the first stage of algorithm, the skin color regions are detected based on the chrominance information. A color segmentation stage is then employed to make skin color regions to be divided into smaller regions which have homogenous color. Then, we use the iterative luminance segmentation to further separate the detected skin region from other skin-colored objects such as hair, clothes, and wood, based on the high variance of the luminance component in the neighborhood of edges of objects. Post-processing is applied to determine whether skin color regions fit the face constrains on density of skin, size, shape and symmetry and contain the facial features such as eyes and mouths. Experimental results show that the algorithm is robust and is capable of detecting multiple faces in the presence of a complex background which contains the color similar to the skin tone.

scholarly journals Content Based Video Retrieval Systems with Local Features: A Survey

2018 ◽  
Vol 7 (S1) ◽  
pp. 58-62
Author(s):  
Gowrisankar Kalakoti ◽  
G. Prabhakaran ◽  
P. Sudhakar
Keyword(s):  
Video Retrieval ◽  
Mixed Media ◽  
Research Issues ◽  
Estimation Techniques ◽  
Video Information ◽  
Retrieval Systems ◽  
Video Retrieval Systems ◽  
Video Recovery ◽  
Recovery Procedure

With the improvement of mixed media information composes and accessible transfer speed there is immense interest of video retrieving frameworks, as clients move from content based recovery frameworks to content based retrieval frameworks. Determination of removed features assume an imperative job in substance based video retrieving paying little mind to video qualities being under thought. This work assists the up and coming analysts in the field of video retrieving with getting the thought regarding distinctive procedures and strategies accessible for the video recovery. These highlights are proposed for choosing, ordering and positioning as indicated by their potential enthusiasm to the client. Great feature determination likewise permits the time and space expenses of the recovery procedure to be lessened. This overview surveys the fascinating highlights that can be separated from video information for ordering and retrieving alongside likeness estimation techniques. We likewise recognize present research issues in territory of content based video retrieving frameworks.

Content-Based Video Indexing and Retrieval

2011 ◽  
pp. 110-144
Author(s):  
Jianping Fan ◽  
Xingquan Zhu ◽  
Jing Xiao
Keyword(s):  
Video Compression ◽  
Large Scale ◽  
Video Retrieval ◽  
Retrieval Systems ◽  
Database Indexing ◽  
Visual Concepts ◽  
Representation Scheme ◽  
High Level ◽  
Video Retrieval Systems ◽  
Content Based Video Retrieval

Recent advances in digital video compression and networks have made videos more accessible than ever. Several content-based video retrieval systems have been proposed in the past.  In this chapter, we first review these existing content-based video retrieval systems and then propose a new framework, called ClassView, to make some advances towards more efficient content-based video retrieval. This framework includes: (a) an efficient video content analysis and representation scheme to support high-level visual concept characterization; (b) a hierarchical video classification technique to bridge the semantic gap between low-level visual features and high-level semantic visual concepts; and (c) a hierarchical video database indexing structure to enable video access over large-scale database. Integrating video access with efficient database indexing tree structures has provided a great opportunity for supporting more powerful video search engines.

Content Based Video Retrieval by Using Distributed Real-Time System Based on Storm

2019 ◽  
Vol 10 (4) ◽  
pp. 60-80 ◽  
Author(s):  
El Mehdi Saoudi ◽  
Abderrahmane Adoui El Ouadrhiri ◽  
Said Jai Andaloussi ◽  
Othmane El Warrak ◽  
Abderrahim Sekkaki
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Video Retrieval ◽  
Real Time System ◽  
Time Processing ◽  
Retrieval Systems ◽  
Long Time ◽  
Video Retrieval Systems ◽  
Content Based Video Retrieval

Time processing is a challenging issue for content-based video retrieval systems, especially when the process of indexing, classifying and retrieving desired and relevant videos is from a huge database. A CBVR system called bounded coordinate of motion histogram (BCMH) has been implemented as a case study. The BCMH offline step requires a long time to complete the learning phase, and the online step falls short in addressing the real-time video processing. To overcome these drawbacks, this article presents a batch-oriented computing based on Apache Hadoop to improve the time processing for the offline step, and a real-time oriented computing based on Apache Storm topologies to achieve a real-time response for the online step. The proposed approach is tested on the HOLLYWOOD2 dataset and the obtained results demonstrate reliability and efficiency of the proposed method.

Context-Based Interpretation and Indexing of Video Data

2008 ◽  
pp. 527-546
Author(s):  
A. Mittal ◽  
Cheong Loong Fah ◽  
Ashraf Kassim ◽  
Krishnan V. Pagalthivarthi
Keyword(s):  
Video Retrieval ◽  
Dynamic Bayesian Network ◽  
Video Data ◽  
Temporal Context ◽  
Single Shot ◽  
Retrieval Systems ◽  
High Level ◽  
Novel Applications ◽  
Video Retrieval Systems ◽  
Video Program

Most of the video retrieval systems work with a single shot without considering the temporal context in which the shot appears. However, the meaning of a shot depends on the context in which it is situated and a change in the order of the shots within a scene changes the meaning of the shot. Recently, it has been shown that to find higher-level interpretations of a collection of shots (i.e., a sequence), intershot analysis is at least as important as intrashot analysis. Several such interpretations would be impossible without a context. Contextual characterization of video data involves extracting patterns in the temporal behavior of features of video and mapping these patterns to a high-level interpretation. A Dynamic Bayesian Network (DBN) framework is designed with the temporal context of a segment of a video considered at different granularity depending on the desired application. The novel applications of the system include classifying a group of shots called sequence and parsing a video program into individual segments by building a model of the video program.

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