Name
Abstract | ||
|---|---|---|
Texture classification is one of the important fields in pattern recognition and machine vision research. LBP method,(13-15) proposed by Ojala, can be used to classify texture images effectively. And the LBP method has rotation-invariant, illumination-invariant, multi-resolution characteristics. But, since the contrast is not considered between neighbor pixels, the correct classification rate produced by this method has been remarkably influenced by light source type and light source orientation. The LMLCP (Local Multiple Layer Contrast Pattern) method, proposed by this paper, maps the contrast value between two near pixels to a rank value, which represent a relative contrast value range, and computes the statistic histogram referring to the work in LBP method. The LMLCP method can bring out the rapid expansion of feature dimension, so a special feature encoding method used in 3DLBP(6) is adopted by this paper. The experiment, which is built based on Outex_TC_00012,(12) demonstrates that the LMLCP can evidently make a more accurate classification rate than LBP method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1142/S0218001411008518 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Texture classification, LBP, illuminate invariant, contrast analysis, 3DLBP | Histogram,Machine vision,Local binary patterns,Artificial intelligence,Feature Dimension,Computer vision,Multi layer,Statistic,Pattern recognition,Pixel,Machine learning,Mathematics,Encoding (memory) | Journal |
Volume | Issue | ISSN |
25 | 1 | 0218-0014 |
Citations | PageRank | References |
2 | 0.40 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hengxin Chen | 1 | 19 | 4.02 |
| Y. Y. Tang | 2 | 416 | 165.12 |
| Bin Fang | 3 | 784 | 53.47 |
| patrick s p wang | 4 | 303 | 47.66 |