Gabor filters for rotation invariant texture classification

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Completed Local Ternary Pattern for Rotation Invariant Texture Classification

Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter’s weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors

Rotation Invariant Texture Classification Using Binary Filter Response Pattern (BFRP)

13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009, Munster, 2-4 September 2009Using 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.Department of ComputingRefereed conference pape

Monogenic-LBP: A new approach for rotation invariant texture classification

2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, 26-29 September 2010Analysis of two-dimensional textures has many potential applications in computer vision. In this paper, we investigate the problem of rotation invariant texture classification, and propose a novel texture feature extractor, namely Monogenic-LBP (M-LBP). M-LBP integrates the traditional Local Binary Pattern (LBP) operator with the other two rotation invariant measures: the local phase and the local surface type computed by the 1st-order and 2 nd-order Riesz transforms, respectively. The classification is based on the image's histogram of M-LBP responses. Extensive experiments conducted on the CUReT database demonstrate the overall superiority of M-LBP over the other state-of-the-art methods evaluated.Department of Computin

Rotation invariant texture classification using adaptive LBP with directional statistical features

2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, 26-29 September 2010Local Binary Pattern (LBP) has been widely used in texture classification because of its simplicity and computational efficiency. Traditional LBP codes the sign of the local difference and uses the histogram of the binary code to model the given image. However, the directional statistical information is ignored in LBP. In this paper, some directional statistical features, specifically the mean and standard deviation of the local absolute difference are extracted and used to improve the LBP classification efficiency. In addition, the least square estimation is used to adaptively minimize the local difference for more stable directional statistical features, and we call this scheme the adaptive LBP (ALBP). By coupling the directional statistical features with ALBP, a new rotation invariant texture classification method is presented. Experiments on a large texture database show that the proposed texture feature extraction and classification scheme could significantly improve the classification accuracy of LBP.Department of ComputingRefereed conference pape