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Entropy Supported Video Indexing for Content based Video Retrieval

by P. M. Kamde, Sankirti Shiravale, S. P. Algur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 17
Year of Publication: 2013
Authors: P. M. Kamde, Sankirti Shiravale, S. P. Algur
10.5120/10169-9974

P. M. Kamde, Sankirti Shiravale, S. P. Algur . Entropy Supported Video Indexing for Content based Video Retrieval. International Journal of Computer Applications. 62, 17 ( January 2013), 1-6. DOI=10.5120/10169-9974

@article{ 10.5120/10169-9974,
author = { P. M. Kamde, Sankirti Shiravale, S. P. Algur },
title = { Entropy Supported Video Indexing for Content based Video Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 17 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number17/10169-9974/ },
doi = { 10.5120/10169-9974 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:12:02.641376+05:30
%A P. M. Kamde
%A Sankirti Shiravale
%A S. P. Algur
%T Entropy Supported Video Indexing for Content based Video Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 17
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The increased in availability and usage of on-line digital video has created a need of automated video content analysis techniques, including indexing and retrieving. Automation of indexing significantly reduces the processing cost while by minimizing tedious work. Traditional video retrieval methods based on video metadata, fail to meet technical challenges due to large and rapid growth of multimedia data, demanding effective retrieval systems. One of the most popular solutions for indexing is extracting the features of video key frames for developing a Content Based Video Retrieval (CBVR) system. CBVR works more effectively as these deals with content of video rather than video metadata. Various features like color, texture, shape can be integrated and used for video indexing and retrieval. Implemented CBVR system is experimented based on integration of texture, color and edge features for video retrieval. Entropy is a texture descriptor used for key frame extraction and video indexing. However entropy, color (RGB) and edge detection algorithms are used for video retrieval. These features are combined in various ways like entropy- edge, entropy- color for result refinement. Dataset is created with the videos from different domains like e-learning, nature, construction etc. By the combination of these features in different ways, we achieved comparative results. Obtained result shows that combining of two or many features gives better retrieval.

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Index Terms

Computer Science
Information Sciences

Keywords

CBVR CBVI Video indexing Video retrieval

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