Automatic Image Annotation Based on Multi-Auxiliary Information

Abstract

This paper introduces an automatic image annotation framework based on multi-auxiliary information which aims at improving the annotation performance. We propose three novel ideas in the framework of annotation: 1) multi-information extraction: besides various visual features, tag co-occurrence, and user interest vector are added to enrich the multi-auxiliary information; 2) initial labeling: based on the traditional term frequency—inverse document frequency model—we utilize the visibility of words and extended tag set to enhance the result of initial labeling and propose a more efficient model, TF-IDF, visibility and extended tag set model; and 3) tag refinement: by considering multi-auxiliary information, including multi-visual content, tag co-occurrence, and user interest similarity, we propose the multi-information all-labels model for tag refinement. The tag refinement process is formalized as an optimization problem by adjusting confidence score set by the initial labeling model. Experimental results demonstrate that, compared with the state-of-the-art methods, our method achieves the best performance on MIR-Flickr data sets, outperforming the second best by 2%.