The Comparison of Different Classifiers for Precision Improvement in Image Retrieval

Abstract

In many researches, valuable studies have been done for feature extraction from images data-base, but because of weak classifiers using, good results have not been achieved. In this paper, different classifiers are compared in order to increase image retrieval system precision. Five different classifiers are used in the paper: the support vector-machine, the MLP neural network, the K-nearest neighbor, the rough neural network, and the rough fuzzy neural network. The rough fuzzy neural network and the rough neural network have not been used in image retrieval implication up to now. The innovation of this research is the using of these classifiers in the image retrieval implication. From the performed test, it is concluded that the rough fuzzy neural network classifier has performed better than other classifiers and increased the image retrieval precision. The COREL image data-base with 1000 images in ten content groups has been used and the classifiers have been compared.

Content Based Image Retrieval System with a Combination of Rough Set and Support Vector Machine

“…First, a nonlinear separable feature space is transformed to a linear separable feature space using a Radial Basis Function Kernel (RBF Kernel). The reason for choosing this kernel is RBF kernel has better results in CBIR systems [12]. The perfect situation is that the SVM can find the hyperplane by maximizing the margin between two classes and no example are in the margin i.e.…”