Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19966
Title: Rotation invariant texture classification using binary filter response pattern (BFRP)
Authors: Guo, Z
Zhang, L 
Zhang, D 
Keywords: LBP
MR8
Texton
Texture classification
Issue Date: 2009
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2009, v. 5702 LNCS, p. 1130-1137 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Using statistical textons for texture classification has shown great success recently. The maximal response 8 (MR8) method, which extracts an 8-dimensional feature set from 38 filters, is one of state-of-the-art rotation invariant texture classification methods. However, this method has two limitations. First, it require a training stage to build a texton library, thus the accuracy depends on the training samples; second, during classification, each 8-dimensional feature is assigned to a texton by searching for the nearest texton in the library, which is time consuming especially when the library size is big. In this paper, we propose a novel texton feature, namely Binary Filter Response Pattern (BFRP). It can well address the above two issues by encoding the filter response directly into binary representation. The experimental results on the CUReT database show that the proposed BFRP method achieves better classification result than MR8, especially when the training dataset is limited and less comprehensive.
Description: 13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009, Munster, 2-4 September 2009
URI: http://hdl.handle.net/10397/19966
ISBN: 3642037666
9783642037665
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-03767-2_137
Appears in Collections:Conference Paper

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