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Abstract

The purpose of classification is to analyze the input data and to develop an correct description or model for each class using the features present in the data for its most effective and efficient use. This paper shows comparison of the classifiers for the efficient CBIR system. Image classification is also a machine learning field that uses algorithms mapping all attributes, variables or inputs - function X space - for the definition of class labeled Y. This algorithm is called the classifier. Basically what a classifier does assign a pre-defined class label to a sample. This paper introduces five classifiers (Naïve Bayes, K-Nearest, Artificial Neural Network, Rough Sets and Support Vector Machine). Among them this CBIR system is implemented a support vector machine classification. SVM depend on the concept of the decision plan that determines the boundaries of the decision. SVM classifiers can be learned from relevant and irrelevant user-generated image for training data.There are two major steps in the classification system such as training step and testing step. Training defines criteria based on recognized features.

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Keywords

CBIR comparison, feature extraction, matching, SVM, Rough set, Naïve Bayes, K-Nearest.