Research Article

Content based Image Retrieval using SVM-ID3

by  Maneela Jain, Pushpendra Singh Tomar, Manish Shrivastava
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Issue 17
Published: February 2014
Authors: Maneela Jain, Pushpendra Singh Tomar, Manish Shrivastava
10.5120/15303-4074
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Maneela Jain, Pushpendra Singh Tomar, Manish Shrivastava . Content based Image Retrieval using SVM-ID3. International Journal of Computer Applications. 87, 17 (February 2014), 35-42. DOI=10.5120/15303-4074 

                        @article{ 10.5120/15303-4074,
                        author  = { Maneela Jain,Pushpendra Singh Tomar,Manish Shrivastava },
                        title   = { Content based Image Retrieval using SVM-ID3 },
                        journal = { International Journal of Computer Applications },
                        year    = { 2014 },
                        volume  = { 87 },
                        number  = { 17 },
                        pages   = { 35-42 },
                        doi     = { 10.5120/15303-4074 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2014
                        %A Maneela Jain
                        %A Pushpendra Singh Tomar
                        %A Manish Shrivastava
                        %T Content based Image Retrieval using SVM-ID3%T 
                        %J International Journal of Computer Applications
                        %V 87
                        %N 17
                        %P 35-42
                        %R 10.5120/15303-4074
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

Content-based image retrieval (CBIR) systems aim to return the most relevant images in a database, according to the user's opinion for a given query. Due to the dynamic nature of the problem, this may change the meaning of relevance among users for a same query. In this ID3 (Decision Tree) based support vector machine (SVM) method proposed to retrieve several features and shorten the semantic gap between low-level visual feature and high-level perception. The analysis of the proposed work is done using MATLAB 2009a simulator.

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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

CBIR Decision tree ID3 SVM Semantic gap.

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