TY - GEN
T1 - Rotation invariant texture classification using binary filter response pattern (BFRP)
AU - Guo, Zhenhua
AU - Zhang, Lei
AU - Zhang, Dapeng
PY - 2009/9/28
Y1 - 2009/9/28
N2 - 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.
AB - 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.
KW - LBP
KW - MR8
KW - Texton
KW - Texture Classification
UR - http://www.scopus.com/inward/record.url?scp=70349305544&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03767-2_137
DO - 10.1007/978-3-642-03767-2_137
M3 - Conference article published in proceeding or book
SN - 3642037666
SN - 9783642037665
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1130
EP - 1137
BT - Computer Analysis of Images and Patterns - 13th International Conference, CAIP 2009, Proceedings
T2 - 13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009
Y2 - 2 September 2009 through 4 September 2009
ER -