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Image Recognition using Texture and Color

Published on February 2014 by Rajivkumar Mente, B V Dhandra, Gururaj Mukarambi
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National Conference on Recent Advances in Information Technology
Foundation of Computer Science USA
NCRAIT - Number 3
February 2014
Authors: Rajivkumar Mente, B V Dhandra, Gururaj Mukarambi
25933393-ce41-4dc0-8c41-468aae5fcd46

Rajivkumar Mente, B V Dhandra, Gururaj Mukarambi . Image Recognition using Texture and Color. National Conference on Recent Advances in Information Technology. NCRAIT, 3 (February 2014), 33-35.

@article{
author = { Rajivkumar Mente, B V Dhandra, Gururaj Mukarambi },
title = { Image Recognition using Texture and Color },
journal = { National Conference on Recent Advances in Information Technology },
issue_date = { February 2014 },
volume = { NCRAIT },
number = { 3 },
month = { February },
year = { 2014 },
issn = 0975-8887,
pages = { 33-35 },
numpages = 3,
url = { /proceedings/ncrait/number3/15158-1426/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Advances in Information Technology
%A Rajivkumar Mente
%A B V Dhandra
%A Gururaj Mukarambi
%T Image Recognition using Texture and Color
%J National Conference on Recent Advances in Information Technology
%@ 0975-8887
%V NCRAIT
%N 3
%P 33-35
%D 2014
%I International Journal of Computer Applications
Abstract

Content–Based Image Retrieval (CBIR) technique uses visual contents to search images in a large scale image databases according to the users' choice. The recognition accuracy depends on the training data set, the potentiality of features and the classifier used. The visual contents of an image such as color, shape, texture etc. are used in CBIR to retrieve the image. Texture is one of the important feature used in CBIR system. It is semi-repetitive arrangements of pixels. Entropy is a statistical measure of randomness that can be used to characterize the texture of the input image. In this paper color and texture feature entropy are combined to form a feature vector for kNN classifier. The algorithm is tested on a database of 2732 images from 6 different fruit classes. The higher recognition and retrieval accuracy is 97. 85% for the proposed algorithm.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Cbir Knn Entropy Rgb Image Retrieval Entropy