#### DMCA

## A completed modeling of local binary pattern operator for texture classification (2010)

Venue: | IEEE Trans. Image Processing |

Citations: | 73 - 6 self |

### Citations

1790 |
Texture feature for image classification
- Haralick, Shanmugam, et al.
- 1973
(Show Context)
Citation Context ... in computer Tvision and pattern recognition. Early texture classification methods focus on the statistical analysis of texture images. The representative ones include the co-occurrence matrix method =-=[1]-=- and the filtering based methods [2]. Kashyap and Khotanzad [3] were among the first researchers to study rotation-invariant texture classification by using a circular autoregressive model. In the ear... |

1297 | Multiresolution grayscale and rotation invariant texture classification with local binary patterns
- Ojala, Pietikainen, et al.
- 2002
(Show Context)
Citation Context ...cal patch to represent features directly. Some works have been recently proposed for scale and affine invariant texture classification by using fractal analysis [9-10] and affine adaption [11-12]. In =-=[13]-=-, Ojala et al proposed to use the Local Binary Pattern (LBP) histogram for rotation invariant texture classification. LBP is a simple yet efficient operator to describe local image pattern, and it has... |

693 |
A comparative study of texture measures with classification based on feature distributions
- Ojala, Pietikainen, et al.
- 1996
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Citation Context ...hape localization [17]. Despite the great success of LBP in computer vision and pattern recognition, its underlying working mechanism still needs more investigation. Before proposing LBP, Ojala et al =-=[18]-=- used the Absolute Gray Level Difference (AGLD) between a pixel and its neighbours to generate textons, and used the histogram of them to represent the image. Later, they proposed LBP [13] to use the ... |

651 | Local features and kernels for classification of texture and object categories: a comprehensive study - Zhang, Marszałek, et al. - 2007 |

585 | Re and texture of real world surfaces
- Dana, Ginneken, et al.
- 1999
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Citation Context ... angles, and the Columbia-Utrecht Reflection and Texture (CUReT) database, which contains 61 classes of real-world textures, each imaged under different combinations of illumination and viewing angle =-=[25]-=-. As in [6-8], we chose 92 sufficiently large images for each class with a viewing angle less than 60 0 in the experiments. A. The methods in comparison As an LBP based scheme, the proposed CLBP is co... |

368 |
Filtering for Texture Classification: A Comparative Study”,
- Randen, Husoy
- 1999
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Citation Context ...ognition. Early texture classification methods focus on the statistical analysis of texture images. The representative ones include the co-occurrence matrix method [1] and the filtering based methods =-=[2]-=-. Kashyap and Khotanzad [3] were among the first researchers to study rotation-invariant texture classification by using a circular autoregressive model. In the early stage, many models were explored ... |

302 | Face recognition with local binary patterns
- Ahonen, Hadid, et al.
- 2004
(Show Context)
Citation Context ...omp.polyu.edu.hk). * Corresponding author, phone: 852-27667271, fax: 852-27740842 representative texture databases [14]. LBP has also been adapted to many other applications, such as face recognition =-=[15]-=-, dynamic texture recognition [16] and shape localization [17]. Despite the great success of LBP in computer vision and pattern recognition, its underlying working mechanism still needs more investiga... |

291 | Dynamic texture recognition using local binary patterns with an application to facial expressions. Pattern Analysis and Machine Intelligence,
- Zhao, Pietikainen
- 2007
(Show Context)
Citation Context ... author, phone: 852-27667271, fax: 852-27740842 representative texture databases [14]. LBP has also been adapted to many other applications, such as face recognition [15], dynamic texture recognition =-=[16]-=- and shape localization [17]. Despite the great success of LBP in computer vision and pattern recognition, its underlying working mechanism still needs more investigation. Before proposing LBP, Ojala ... |

274 | Enhanced local texture feature sets for face recognition under difficult lighting conditions
- Tan, Triggs
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Citation Context ...Difference (SGLD) and regarded LBP as a simplified operator of SGLD by keeping sign patterns only. Ahonen and Pietikäinen [20] analyzed LBP from a viewpoint of operator implementation. Tan and Triggs =-=[21]-=- proposed Local Ternary Pattern (LTP) to quantize the difference between a pixel and its neighbours into three levels. Although some variants of LBP, such as derivative-based LBP [17], dominant LBP [2... |

232 | A statistical approach to texture classification from single images,”
- Varma, Zisserman
- 2005
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Citation Context ...e early stage, many models were explored to study rotation invariance for texture classification, including hidden Markov model [4] and Gaussian Markov random filed [5]. Recently, Varma and Zisserman =-=[6]-=- proposed to learn a rotation invariant texton dictionary from a training set, and then classify the texture image based on its texton distribution. Later, Varma and Zisserman [7-8] proposed another t... |

208 | A sparse texture representation using local affine regions - Lazebnik, Schmid, et al. - 2005 |

173 |
Combination of multiple classifiers using local accuracy estimates
- Woods, Kegelmeyer, et al.
- 1997
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Citation Context ... for TC10, TC12 “t” and TC12 “h” respectively. The proposed multi-scale scheme could be regarded as a simple sum fusion. Better performance can be expected if more advanced fusion techniques are used =-=[26]-=-. The proposed multi-scale CLBP is simple and fast to build the feature histogram; however, its feature size is a little higher than that of VZ_MR8. For example, the dimension of multi-scale CLBP _ S ... |

171 | Texture classification: Are filter banks necessary - Varma, Zisserman - 2003 |

98 | Outex - new framework for empirical evaluation of texture analysis algorithms.
- Ojala, Maenpaa, et al.
- 2002
(Show Context)
Citation Context ...cal calculation, we can derive Es=� 2 and Em=4� 2 . Obviously, Es is only of Em. To further validate this conclusion, we calculated Es and Em for 864 texture images selected from the Outex database =-=[14]-=-. The average values of Es and Em are 98 and 403, respectively. This is exactly identical to the above mathematical derivation. From the above analysis, we see that dp can be more accurately approxima... |

90 |
Schmid “Description of interest regions with local binary patterns”
- Heikkila, Pietikainen, et al.
- 2009
(Show Context)
Citation Context ...ern (LTP) to quantize the difference between a pixel and its neighbours into three levels. Although some variants of LBP, such as derivative-based LBP [17], dominant LBP [22] and center-symmetric LBP =-=[23]-=-, have been proposed recently, there still remain some questions to be better answered for LBP. For example, why the simple LBP code could convey so much discriminant information of the local structur... |

85 | A statistical approach to material classification using image patch exemplars. - Varma, Zisserman - 2009 |

85 |
Applications of Universal Context Modelling to Lossless Compression of Grey-Scale Images”,
- Weinberger, Rissanen, et al.
- 1996
(Show Context)
Citation Context ...of sp and mp, we cannot directly reconstruct dp by leaving one of sp and mp out. It is well accepted that the difference signal dp can be well modeled by Laplace distribution Qx ( ) �exp �� x/ �� / � =-=[24]-=-, where parameter � depends on the image content. Here we apply some prior knowledge to the probability distribution of sp and mp. It can be observed that the sign component sp follows a Bernoulli n�1... |

75 |
A model-based method for rotation invariant texture classification,
- Kashyap, Khotanzad
- 1986
(Show Context)
Citation Context ...ssification methods focus on the statistical analysis of texture images. The representative ones include the co-occurrence matrix method [1] and the filtering based methods [2]. Kashyap and Khotanzad =-=[3]-=- were among the first researchers to study rotation-invariant texture classification by using a circular autoregressive model. In the early stage, many models were explored to study rotation invarianc... |

49 | A.C.S.: Dominant local binary patterns for texture classification.
- Liao, Law, et al.
- 2009
(Show Context)
Citation Context ...1] proposed Local Ternary Pattern (LTP) to quantize the difference between a pixel and its neighbours into three levels. Although some variants of LBP, such as derivative-based LBP [17], dominant LBP =-=[22]-=- and center-symmetric LBP [23], have been proposed recently, there still remain some questions to be better answered for LBP. For example, why the simple LBP code could convey so much discriminant inf... |

40 | Texture discrimination with multidimensional distributions of signed gray level differences.
- Ojala, Valkealahti, et al.
- 2001
(Show Context)
Citation Context ...rate textons, and used the histogram of them to represent the image. Later, they proposed LBP [13] to use the sign, instead of magnitude, of the difference to represent the local pattern. Ojala et al =-=[19]-=- also proposed a multidimensional distribution of Signed Gray Level Difference (SGLD) and regarded LBP as a simplified operator of SGLD by keeping sign patterns only. Ahonen and Pietikäinen [20] analy... |

39 |
Shape localization based on statistical method using extended local binary pattern,
- Huang, Wang
- 2004
(Show Context)
Citation Context ..., fax: 852-27740842 representative texture databases [14]. LBP has also been adapted to many other applications, such as face recognition [15], dynamic texture recognition [16] and shape localization =-=[17]-=-. Despite the great success of LBP in computer vision and pattern recognition, its underlying working mechanism still needs more investigation. Before proposing LBP, Ojala et al [18] used the Absolute... |

38 |
Rotation and Gray Scale Transform Invariant Texture Identification Using Wavelet Decomposition and Hidden Markov Model.
- Chen, Kundu
- 1994
(Show Context)
Citation Context ...t texture classification by using a circular autoregressive model. In the early stage, many models were explored to study rotation invariance for texture classification, including hidden Markov model =-=[4]-=- and Gaussian Markov random filed [5]. Recently, Varma and Zisserman [6] proposed to learn a rotation invariant texton dictionary from a training set, and then classify the texture image based on its ... |

28 | Locally invariant fractal features for statistical texture classification - Varma, Garg - 2007 |

21 | A projective invariant for texture, in
- Xu, Hui, et al.
- 2006
(Show Context)
Citation Context ...ructed from them. � � . The local difference vector � � 0,..., P 1 Fig. 2 shows an example. Fig. 2a is the original 3�3 local structure with central pixel being 25. The difference vector (Fig. 2b) is =-=[3, 9,-13,-16,-15, 74, 39, 31]-=-. After LDSMT, the sign vector (Fig. 2c) is [1,1,-1,-1,-1,1,1,1] and the magnitude vector (Fig. 2d) is[3, 9, 13, 16, 15, 74, 39, 31]. It is clearly seen that the original LBP uses only the sign vector... |

16 |
Image description using joint distribution of filter bank responses
- Ahonen, Pietikäinen
(Show Context)
Citation Context ...et al [19] also proposed a multidimensional distribution of Signed Gray Level Difference (SGLD) and regarded LBP as a simplified operator of SGLD by keeping sign patterns only. Ahonen and Pietikäinen =-=[20]-=- analyzed LBP from a viewpoint of operator implementation. Tan and Triggs [21] proposed Local Ternary Pattern (LTP) to quantize the difference between a pixel and its neighbours into three levels. Alt... |

2 |
Gaussian VZ-MRF rotationinvariant features for image classification
- Deng, Clausi
- 2004
(Show Context)
Citation Context ...ircular autoregressive model. In the early stage, many models were explored to study rotation invariance for texture classification, including hidden Markov model [4] and Gaussian Markov random filed =-=[5]-=-. Recently, Varma and Zisserman [6] proposed to learn a rotation invariant texton dictionary from a training set, and then classify the texture image based on its texton distribution. Later, Varma and... |