# A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model

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## Abstract

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## 1. Introduction

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_{, R}[24] with neighborhoods of different sizes, where P is the sample point number in a circle area with a radius of R. Since LBP is a two-value model, which cannot describe more detailed information, Tan [25] extends the two-value model to the three-value model and proposes a novel local feature description method, local ternary patterns (LTP). Furthermore, many variants from the basic LBP have been presented, including local phase quantization (LPQ) [26], local derivative pattern (LDP) [27], local difference binary (LDB) [28], local line directional pattern (LLDP) [29], local binary pattern of pyramid transform domain (PLBP) [30], local tetra patterns (LTrPs) [31], dominant local binary pattern (DLBP) [32], binary robust independent elementary features (BRIEF) [33], local tri-directional patterns (LTPs) [34], local convex-and-concave pattern (LCP) [35] multi-scale local binary patterns (MSLBP) [36] and etc. In addition, motivated by image moments and local binary patterns, some novel texture descriptors have been proposed, such as local Tchebichef moments (LTMs) [37], moment-based local binary patterns (MLBP) [38] and etc. Nanni [39,40] has presented region-based approaches with a co-occurrence matrix, which have had promising results in several medical datasets. Gabor wavelet filters are an excellent feature representation that is insensitive to illumination and expression changes. There are many Gabor feature extraction methods which have shown remarkable performances and wide applications [41,42,43,44,45,46,47,48], such as local normalization entropy-like weighted Gabor features [42], local Gabor binary patterns (LGBP) [43], local Gabor XOR patterns (LGXP) [44], Gabor wavelets and local binary pattern [45], Gabor wavelets combined with volumetric fractal dimension [46], the combined method with the joint of local binary pattern (LBP), local phase quantization (LPQ) and fuses Gabor filters [26]. Since the computation amount of Gabor frames is very high, some accelerated Gabor methods have been studied, such as accelerated Gabor frames [47], fusion of multi-channels classifier [48].

- (1)
- Conventional LBP computes the relationship between one image’s center pixel value and its neighbor pixel value, and always only utilizes the center pixel’s direction information. LBP cannot obtain more detailed direction information from other neighborhood pixels, and thus is sensitive to noise. To overcome these defects, we propose a novel patch-structure direction pattern (PDP) method, which can extract richer feature information and be insensitive to noise.
- (2)
- To further improve the effectiveness of PDP, we introduce it into multi-channel Gabor space and get an improved method called GGDP, which can better describe multi-direction and multi-scale texture information.
- (3)
- In the traditional classification process, the GGDP feature of each Gabor sub-image should be concatenated and measured. To make the measurement of feature distance more accurate, WDMM is proposed for measuring every GGDP feature of the Gabor sub-image distance and use weighted computing for the final distance with sub-image information content.

## 2. Algorithm Description

#### 2.1. PDP

#### 2.2. GGDP

#### 2.3. WDMM

## 3. Experiments

#### 3.1. Performance of the Proposed Method

#### 3.1.1. Discussion of Computational Time

#### 3.1.2. Discussion on Classification

#### 3.2. Experiments and Analysis on CMUPIE Database

#### 3.3. Experiments and Analysis on the ORL Database

#### 3.4. Experiments and Analysis on YALE B Database

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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Method Abbreviation | Method Explanation |
---|---|

LBP [23] | Basic LBP features |

LBP1 [24] | LBP (8, 1) features |

LBP2 [24] | LBP (8, 2) features |

LTP [25] | LTP features |

LG [40] | Local Gabor |

LGBP [42] | Local Gabor Binary Pattern |

LLDP [29] | Local Line Directional Pattern |

GGDP | Generalized Gabor Direction Patterns |

Descriptor | Feature Dimension | Feature Extract Times (ms) |
---|---|---|

LBP [1] | 256 | 97.4 |

LG [40] | 393,216 | 294.2 |

LGBP [42] | 6144 | 326.4 |

GGDP | 6144 | 386.5 |

Recognition Methods | Training Sample Numbers | |||||
---|---|---|---|---|---|---|

1 | 2 | 4 | 6 | 8 | 10 | |

GGDP + NN | 44.71% | 56.08% | 62.65% | 67.94% | 73.14% | 81.08% |

GGDP + SVM | 45.98% | 76.76% | 81.76% | 87.35% | 88.33% | 91.08% |

GGDP + WDMM | 56.76% | 75.39% | 83.73% | 86.18% | 90.98% | 93.82% |

**Table 4.**Recognition rates of methods on CMUPLE (Carnegie Mellon University pose, illumination, and expression) with different training sample numbers.

Recognition Methods | Training Sample Numbers | |||||
---|---|---|---|---|---|---|

1 | 2 | 4 | 6 | 8 | 10 | |

LBP | 40.29% | 44.41% | 48.92% | 55.98% | 62.45% | 68.14% |

LBP1 | 46.18% | 47.84% | 49.71% | 58.73% | 64.02% | 73.92% |

LBP2 | 47.35% | 48.92% | 50.20% | 61.57% | 65.10% | 75.10% |

LTP | 49.31% | 50.59% | 51.76% | 63.53% | 66.86% | 76.67% |

LG | 50.20% | 55.59% | 60.10% | 73.14% | 79.12% | 87.45% |

LGBP | 54.02% | 67.35% | 72.06% | 80.29% | 83.43% | 91.86% |

LLDP | 55.49% | 72.45% | 85.20% | 85.78% | 89.31% | 91.27% |

GGDP | 56.76% | 75.39% | 83.73% | 86.18% | 90.98% | 93.82% |

Recognition Methods | Training Sample Numbers | |||||
---|---|---|---|---|---|---|

1 | 2 | 3 | 4 | 5 | 6 | |

LBP | 53.75% | 55.50% | 64.00% | 76.50% | 83.75% | 84.25% |

LBP1 | 55.75% | 56.00% | 65.50% | 81.00% | 85.25% | 85.50% |

LBP2 | 58.50% | 59.00% | 65.25% | 81.50% | 88.00% | 88.50% |

LTP | 60.25% | 61.50% | 69.00% | 83.50% | 87.25% | 88.25% |

LG | 64.25% | 69.75% | 72.25% | 83.00% | 88.00% | 91.50% |

LGBP | 65.75% | 70.75% | 75.50% | 87.00% | 90.75% | 95.25% |

LLDP | 63.25% | 72.25% | 75.75% | 86.50% | 92.25% | 96.50% |

GGDP | 70.25% | 74.75% | 78.00% | 90.25% | 93.25% | 98.00% |

Recognition Methods | Training Sample Numbers | |||||
---|---|---|---|---|---|---|

1 | 2 | 4 | 8 | 16 | 32 | |

LBP | 37.91% | 38.22% | 48.94% | 51.72% | 63.19% | 63.53% |

LBP1 | 42.31% | 42.69% | 52.44% | 53.09% | 69.25% | 66.06% |

LBP2 | 43.63% | 44.44% | 52.78% | 53.84% | 64.25% | 67.91% |

LTP | 44.53% | 48.84% | 53.22% | 55.09% | 65.72% | 69.88% |

LG | 49.59% | 53.01% | 55.48% | 68.77% | 75.21% | 78.36% |

LGBP | 53.56% | 58.36% | 66.99% | 73.56% | 77.95% | 82.60% |

LLDP | 59.72% | 66.59% | 69.66% | 70.34% | 79.34% | 85.78% |

GGDP | 58.63% | 64.52% | 72.60% | 78.22% | 81.37% | 86.44% |

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**MDPI and ACS Style**

Chen, T.; Zhao, X.; Dai, L.; Zhang, L.; Wang, J.
A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model. *Symmetry* **2016**, *8*, 109.
https://doi.org/10.3390/sym8110109

**AMA Style**

Chen T, Zhao X, Dai L, Zhang L, Wang J.
A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model. *Symmetry*. 2016; 8(11):109.
https://doi.org/10.3390/sym8110109

**Chicago/Turabian Style**

Chen, Ting, Xiangmo Zhao, Liang Dai, Licheng Zhang, and Jiarui Wang.
2016. "A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model" *Symmetry* 8, no. 11: 109.
https://doi.org/10.3390/sym8110109