Image Tagging by Exploiting Feature Correlation

Abstract

Image tagging is a task that automatically assigns the query image with semantic keywords called tags. Since tags and image visual content are represented in different feature space, how to merge the multiple features by their correlation to tag the query image is an important problem. However, most of existing approaches merge the features by using a relatively simple mechanism rather than fully exploiting the correlations between different features. In this paper, we propose a new approach to fusing different features and their correlation simultaneously for image tagging. Specifically, we employ a Feature Correlation Graph to capture the correlations between different features in an integrated manner, which take features as nodes and their correlations as edges. Then, a revised probabilistic model based on Markov Random Field is used to describe the graph for evaluating tag’s relevance to query image. Experiments on large real-life corpuses collected from Flickr indicate the superiority of our proposed approach.