Anzeige
Erschienen in:
2009 | OriginalPaper | Buchkapitel
Image Categorization Based on a Hierarchical Spatial Markov Model
verfasst von : Lihua Wang, Zhiwu Lu, Horace H. S. Ip
Erschienen in: Computer Analysis of Images and Patterns
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.