Maximum entropy random fields for texture analysis

Authors:
Xiangyu Yang;Jun Liu
Affiliations:
Information System Research Lab, S2-B3a-06, School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, Singapore 639798, Singapore;Information System Research Lab, S2-B3a-06, School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, Singapore 639798, Singapore
Venue:
Pattern Recognition Letters
Year:
2002

Citing 5
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Abstract

Texture is often used as a region descriptor in image analysis and computer vision. Texture analysis is an important research area with important applications to digital libraries and multi-media databases. This paper will focus on a novel texture model named the maximum entropy random field (MERF). The MERF is a random field built upon multi-resolution filters with the maximum entropy (ME) method. Its joint probability distribution can be considered as a Gibbs distribution. The multi-resolution filters play a central role in the MERF: they define the potential function in the Gibbs distribution of the random field, and they can be used to extract texture features in various orientations and scales. The experiments of texture synthesis illustrate using the MERF to describe textures. The experiments of texture retrieval compare the MERF based feature with Gabor filters based feature and the multi-resolution autoregressive based feature using the Brodatz database, which indicates that the MERF features provide the best pattern retrieval accuracy.