Improving automatic image annotation based on word co-occurrence

Accuracy of current automatic image labeling methods is under the requirements of annotation-based image retrieval systems. The performance of most of these labeling methods is poor if we just consider the most relevant label for a given region. However, if we look within the set of the top-k candidate labels for a given region, accuracy of most of these systems is improved. In this paper we take advantage of this fact and propose a method (NBI) based on word co-occurrences that uses the naive Bayes formulation for improving automatic image annotation methods. Our approach utilizes co-occurrence information of the candidate labels for a region with those candidate labels for the other surrounding regions, within the same image, for selecting the correct label. Co-occurrence information is obtained from an external collection of manually annotated images: the IAPR-TC12 benchmark. Experimental results using a k-nearest neighbors method as our annotation system, give evidence of significant improvements after applying the NBI method. NBI is efficient since the co-occurrence information was obtained off-line. Furthermore, our method can be applied to any other annotation system that ranks labels by their relevance.