Semantic image classification using statistical local spatial relations model

Authors:
Dongfeng Han;Wenhui Li;Zongcheng Li
Affiliations:
College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, People's Republic of China;College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, People's Republic of China;School of Engineering Technology, Shandong University of Technology, Zibo, People's Republic of China
Venue:
Multimedia Tools and Applications
Year:
2008

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Abstract

In this paper, a statistical model called statistical local spatial relations (SLSR) is presented as a novel technique of a learning model with spatial and statistical information for semantic image classification. The model is inspired by probabilistic Latent Semantic Analysis (PLSA) for text mining. In text analysis, PLSA is used to discover topics in a corpus using the bag-of-word document representation. In SLSR, we treat image categories as topics, therefore an image containing instances of multiple categories can be modeled as a mixture of topics. More significantly, SLSR introduces spatial relation information as a factor which is not present in PLSA. SLSR has rotation, scale, translation and affine invariant properties and can solve partial occlusion problems. Using the Dirichlet process and variational Expectation-Maximization learning algorithm, SLSR is developed as an implementation of an image classification algorithm. SLSR uses an unsupervised process which can capture both spatial relations and statistical information simultaneously. The experiments are demonstrated on some standard data sets and show that the SLSR model is a promising model for semantic image classification problems.