Image Categorization Based on a Hierarchical Spatial Markov Model

  • Authors:

  • Lihua Wang;Zhiwu Lu;Horace H. Ip

  • Affiliations:

  • Image Computing Group, Department of Computer Science Center for Innovative Applications of Internet and Multimedia Technologies (AIMtech Centre), City University of Hong Kong, Hong Kong;Image Computing Group, Department of Computer Science Center for Innovative Applications of Internet and Multimedia Technologies (AIMtech Centre), City University of Hong Kong, Hong Kong;Image Computing Group, Department of Computer Science Center for Innovative Applications of Internet and Multimedia Technologies (AIMtech Centre), City University of Hong Kong, Hong Kong

  • Venue:
  • CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
  • Year:
  • 2009

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Abstract

In this paper, we propose a Hierarchical Spatial Markov Model (HSMM) for image categorization. We adopt the Bag-of-Words (BoW) model to represent image features with visual words, thus avoiding the heavy work of manual annotation in most Markov model based approaches. Our HSMM is designed to describe the spatial relations of these visual words by modeling the distribution of transitions between adjacent words over each image category. A novel idea of semantic hierarchy is exerted in the model to represent the composition relationship of visual words at semantic level. Experiments demonstrate that our approach outperforms Bayesian hierarchical model based categorization approach with 12.5% and it also performs better than the previous Markov model based approach with 11.8% on average.