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Image Similarity Measurement using Shape Feature

Published on September 2015 by Shweta R. Patil, V.s. Patil
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National Conference on Advances in Communication and Computing
Foundation of Computer Science USA
NCACC2015 - Number 1
September 2015
Authors: Shweta R. Patil, V.s. Patil
af543ba9-8679-4229-a863-021598a52a82

Shweta R. Patil, V.s. Patil . Image Similarity Measurement using Shape Feature. National Conference on Advances in Communication and Computing. NCACC2015, 1 (September 2015), 6-9.

@article{
author = { Shweta R. Patil, V.s. Patil },
title = { Image Similarity Measurement using Shape Feature },
journal = { National Conference on Advances in Communication and Computing },
issue_date = { September 2015 },
volume = { NCACC2015 },
number = { 1 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 6-9 },
numpages = 4,
url = { /proceedings/ncacc2015/number1/22321-3010/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Communication and Computing
%A Shweta R. Patil
%A V.s. Patil
%T Image Similarity Measurement using Shape Feature
%J National Conference on Advances in Communication and Computing
%@ 0975-8887
%V NCACC2015
%N 1
%P 6-9
%D 2015
%I International Journal of Computer Applications
Abstract

In this paper, we describe an incipient method for image retrieval predicated on the local invariant shape feature, designated scalable shape context. The feature utilizes the Harris-Laplace corner to locat the fix points and coinside scale in the animal and flower image. Then, we utilize shape context to explain the local shape. Correspondence of feature points is achieved by a weighted bipartite graph matching algorithm and the homogeneous attribute between the query and the indexing image is presented by the match cost. The practical results show that our method is efficient than shape context and SIFT for the animal and flower image retrieval.

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Index Terms

Computer Science
Information Sciences

Keywords

Local Invariant Shape Feature Key Points Graph Matching.