A Novel Image Classification Algorithm Based on Graph Theory

Abstract

In this paper the bag-of-words model is applied to image classification and improves the existing problems of the traditional bag-of-words method. We propose a method of combination of corner detection and graph theory for ROI region extraction and fuzzy membership degree. First using corner detection for images, then the ROI region is defined by the method of graph theory. Then the SIFT features of the ROI region are extracted and the visual dictionary is generated. The visual dictionary can be more accurate to describe the image features, which can reduce the influence of background information and other interference information. Secondly, the concept of fuzzy membership function and information of feature space is introduced to improve the image of the visual histogram. Finally, support vector machine classifier is used to classify. Through the experiment of the Caltech 100 database, the result shows that the method improves the accuracy of classification compared with the traditional method.