Improving Automatic Image Annotation Based on Word Co-occurrence

Abstract. 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 (NBIC) based on word co-occurrence that uses the na¨ıve 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 evid...

• Type: Conference
• Year: 2007
• Where: AMR
• Authors: Hugo Jair Escalante, Manuel Montes-y-Gómez, Luis Enrique Sucar
• Keywords: AMR 2007, Candidate Labels, Co-occurrence Information, Labels, Multimedia