A Novel Framework for Image Categorization and Automatic Annotation

Abstract

Image classification and automatic annotation could be treated as effective solutions to enable keyword-based semantic image retrieval. Traditionally, they are investigated in different models separately. In this chapter, we propose a novel framework uniting image classification and automatic annotation by learning semantic concepts of image categories. To choose representative features, feature selection strategy is proposed and visual keywords are constructed, including discrete method and continuous method. Based on the selected features, the Integrated Patch (IP) model is proposed to describe the image category. As a generative model, the IP model describes the appearance of the combination of the visual keywords, considering the diversity of the object. The parameters are estimated by EM algorithm. The experimental results on Corel image dataset and Getty Image Archive demonstrate that the proposed feature selection and image description model are effective in image categorization and automatic image annotation, respectively.