An approach for image classification using wavelet transforms and color based feature extraction methods against various data mining classifiers

A. C. Hemanth Vinay, Preetham Kumar

Research output: Contribution to journalArticle

Abstract

Image classification is an important task in multimedia database and in computer vision. There are several methods for classifying images, one among them is content based image retrieval. Content refers to color, shape and texture. A recent study tells that image classification accuracy can be improved by using orthogonal transforms against various data mining classifiers. In this paper, we use orthogonal transforms such as Fast Walsh and Haar wavelet transforms against various data mining classifiers, and also include feature extraction methods like grid based color moment, Color Histogram and Color Coherence Vector against various data mining classifiers. As a result it shows that the Grid color moment gives better accuracy using Naive Bayes classifier.

Original languageEnglish
Pages (from-to)204-209
Number of pages6
JournalInternational Journal of Applied Engineering Research
Volume10
Issue number69
Publication statusPublished - 01-01-2015

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Image classification
Wavelet transforms
Data mining
Feature extraction
Classifiers
Color
Image retrieval
Computer vision
Textures

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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An approach for image classification using wavelet transforms and color based feature extraction methods against various data mining classifiers. / Hemanth Vinay, A. C.; Kumar, Preetham.

In: International Journal of Applied Engineering Research, Vol. 10, No. 69, 01.01.2015, p. 204-209.

Research output: Contribution to journalArticle

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