Supervised Machine Learning based Medical Image Annotation and Retrieval.
Md Mahmudur Rahman, Bipin C. Desai, Prabir Bhattacharya · 2005
This paper presents the approaches and experimental results of image annotation and retrieval in our first participation of ImageCLEFmed 2005. In this work, we investigate a supervised learning approach to associate low-level global image features with their high level visual and/or semantic categories for image annotation and retrieval. For automatic image annotation, we represent input images through a large dimensional feature vector of texture, edge and shape features. A multi-class classification system based on pairwise coupling of several binary support vector machine (SVM) is trained on this input to predict the categories of test images, which will be effective for later annotation. For visual only retrieval, we utilize a low dimensional feature vector of color, texture and edge features based on principal component analysis (PCA) and category specific feature distribution information in a statistical similarity measure function. Based on the online category prediction of query and database images by the multi-class SVM classifier, pre-computed category specific first and second order statistical parameters are utilized in Bhattacharyya distance measure on the assumption that distributions are multivariate Gaussian. Experimental results of both image annotation and retrieval are reported in this paper.