Improving Automatic Image Annotation Based on Word Co-occurrence

  • H. Jair Escalante
  • Manuel Montes
  • L. Enrique Sucar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4918)

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 (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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • H. Jair Escalante
    • 1
  • Manuel Montes
    • 1
  • L. Enrique Sucar
    • 1
  1. 1.Computer Science DepartmentNational Institute of Astrophysics, Optics and ElectronicsPueblaMéxico

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