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.
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Wang, L., Lu, Z., Ip, H.H.S. (2009). Image Categorization Based on a Hierarchical Spatial Markov Model. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_93
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DOI: https://doi.org/10.1007/978-3-642-03767-2_93
Publisher Name: Springer, Berlin, Heidelberg
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