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

Vinay, Hemanth A C and Kumar, Preetham (2015) An approach for image classification using wavelet transforms and color based feature extraction methods against various data mining classifiers. In: International Conference on Computing Paradigms, 24-25 July 2015.

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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

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Wavelet Transforms; Grid color moment; Color Histogram; Color Coherence Vector; Data mining Classifiers
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 31 Dec 2015 12:18
Last Modified: 31 Dec 2015 12:18
URI: http://eprints.manipal.edu/id/eprint/144981

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