Co-training for search-based automatic image annotation

Recently, motivated by the search technology, a data-driven annotation approach turns up to be effective [8, 9]. Given a query image and a labeled keyword, X. J. Wang et al [8] apply the search result cluster (SRC) algorithm into a three-layer annotation model. In [9], an improvement on [8] is made by C. Wang et al, who propose a scalable search-based approach to annotate the web personal images only provided with a query image. One of advantages of search-based image annotation is to avoid a complex supervised learning since it is learned from the labeled textual keywords of retrieved images, which have been labeled accurately. Furthermore, the annotation is taken on a high-level semantic property and is suitable for a scalable image database. However, the average precision of annotation [8, 9] is unsatisfactory because the retrieved labeled images are involved in too many non-relevant images, which lead to incomplete or improper annotation results. Obviously, the retrieval effectiveness is a crucial element in search-based annotation approach to some extent, and directly influences the performance of image annotation. Hence, to give higher retrieval accuracy under current technique, it is prone to applying a semi-supervised learning fashion into the search-based image annotation procedure. Thus, we propose a novel annotation framework based on the co-training strategy, the goal of which is to exploit more relevant images with related to the unlabeled image for annotating. Based on learning two independent classifiers on their additional training set, each classifier can select some most confident images to enhance the generalization ability of the other one. Since each classifier is optimized gradually in a co-training manner, more and more relevant images can be explored automatically during the training process, which directly boosts the annotation performance. In addition, each relevant image makes different contribution for the final annotation result hence a corresponding weight is assigned to it, which is given by the probability output of the corresponding classification. Furthermore, to decide the final reliability of keywords to be annotated, the histogram of retrieved keywords is proposed to guarantee the scalability of annotation. With the promoted search precision based on the co-training strategy, the experimental analyses demonstrate the improvement of annotation performance. The paper is organized by the following sections. Section 2 introduces the framework of co-training based automatic image annotation and its mathematics expression. The proposed co-training algorithm is dealt with in section 3. The automatic annotation procedure is …