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 naïve 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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Escalante, H. J., Montes, M., & Sucar, L. E. (2008). Improving automatic image annotation based on word co-occurrence. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4918 LNCS, pp. 57–70). https://doi.org/10.1007/978-3-540-79860-6_5
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