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Neuro-Fuzzy based Image Retrieval System with Improved Shape and Texture Features

Published on July 2016 by D. B. Kshirsagar, U. V. Kulkarni
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International Conference on Internet of Things, Next Generation Networks and Cloud Computing
Foundation of Computer Science USA
ICINC2016 - Number 2
July 2016
Authors: D. B. Kshirsagar, U. V. Kulkarni
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D. B. Kshirsagar, U. V. Kulkarni . Neuro-Fuzzy based Image Retrieval System with Improved Shape and Texture Features. International Conference on Internet of Things, Next Generation Networks and Cloud Computing. ICINC2016, 2 (July 2016), 18-24.

@article{
author = { D. B. Kshirsagar, U. V. Kulkarni },
title = { Neuro-Fuzzy based Image Retrieval System with Improved Shape and Texture Features },
journal = { International Conference on Internet of Things, Next Generation Networks and Cloud Computing },
issue_date = { July 2016 },
volume = { ICINC2016 },
number = { 2 },
month = { July },
year = { 2016 },
issn = 0975-8887,
pages = { 18-24 },
numpages = 7,
url = { /proceedings/icinc2016/number2/25531-4803/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Internet of Things, Next Generation Networks and Cloud Computing
%A D. B. Kshirsagar
%A U. V. Kulkarni
%T Neuro-Fuzzy based Image Retrieval System with Improved Shape and Texture Features
%J International Conference on Internet of Things, Next Generation Networks and Cloud Computing
%@ 0975-8887
%V ICINC2016
%N 2
%P 18-24
%D 2016
%I International Journal of Computer Applications
Abstract

A generalized Neuro-Fuzzy based Content Based Image Retrieval (CBIR) system is proposed. The system is trained for colour, texture and shape features using General Fuzzy Min-Max Neural Network (GFMNN). Flexibility and robustness is achieved by accepting any number and types of different input features as well with the concept of class labels assigned for each hyperbox. The existing architecture is simplified and the system is trained in pure clustering mode which helps in reducing the computational complexity. By controlling user parameters the system can categorize images as per the users need. With modified texture and shape features combined with colour features, the proposed CBIR system gives an efficient automated retrieval of similar images.

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Index Terms

Computer Science
Information Sciences

Keywords

Cbir Gfmnn Hyperbox Spatial Grey Level Dependency Matrix (sgldm) Fourier Descriptors