Image Object Classification Using Scale Invariant Feature Transform Descriptor with Support Vector Machine Classifier with Histogram Intersection Kernel

Recently much attention have been paid to region of interests in an image as they are useful in bridging the gap between high level image semantics and low level image features. In this paper we have proposed a method for classification of image objects produced by a standard image segmentation algorithm using multiclass support vector machine classifier integrated with histogram intersection kernel. SIFT is a relatively new feature descriptor which describes a given object in terms of a number of interest points. They are invariant to scaling, translation and partially invariant to illumination changes. This paper primarily focuses on the design of a fast and efficient image object classifier by combining the robust SIFT feature descriptor with intersection kernel SVM which is comparatively better than the existing kernel functions in terms of resource utilization. The experimental results show that the proposed method has good generalization accuracy.