Active learning based image annotation

Automatic image annotation is the computer vision task of assigning a set of appropriate textual tags to a novel image. The aim is to eventually bridge the semantic gap of visual and textual representations with the help of these tags. This also has applications in designing scalable image retrieval systems and providing multilingual interfaces. Though a wide varieties of powerful machine learning algorithms have been explored for the image annotation problem in the recent past, nearest neighbor techniques still yield superior results to them. A challenge ahead of the present day annotation schemes is the lack of sufficient training data. In this paper, an active Learning based image annotation model is proposed. We leverage the image-to-image and image-to-tag similarities to decide the best set of tags describing the semantics of an image. The advantages of the proposed model includes: (a). It is able to output the variable number of tags for images which improves the accuracy. (b). It is effectively able to choose the difficult samples that needs to be manually annotated and thereby reducing the human annotation efforts. Studies on Corel and IAPR TC-12 datasets validate the effectiveness of this model.