Abstract

In this paper, we present a simple yet efficient and effective multi-resolution approach to gray-scale and rotation invariant texture classification. Given a texture image, we at first convolve it with J Gabor filters sharing the same parameters except the parameter of orientation. Then by binarizing the obtained responses, we can get J bits at each location. Then, each location can be assigned a unique integer, namely “rotation invariant binary Gabor pattern (BGPri)”, formed from J bits associated with it using some rule. The classification is based on the image’s histogram of its BGPris at multiple scales. Using BGPri, there is no need for a pre-training step to learn a texton dictionary, as required in methods based on clustering such as MR8. Extensive experiments conducted on the CUReT database demonstrate the overall superiority of BGP_ri over the other state-of-the-art texture representation methods evaluated.

Source Code

The source code can be downloaded here: BGP.m.

Algorithm

In this paper, we propose a novel training-free rotation invariant texture representation scheme. Here training-free means that in our method there is no need for a pre-training step to learn a texton dictionary as MR8 does. Our idea is inspired by the success of LBP, such a simple yet powerful texture descriptor. From the definition of LBP it can be known that LBP for a central pixel is totally decided by the signs of differences between it and its neighboring pixels. But, each sign used in LBP is binarized from the difference of two single pixels so it may be sensitive to noise. To improve it, we can use difference between regions to replace difference between two single pixels, which will be more robust intuitively. Gabor filter is an ideal tool to this end, which can calculate the difference between regions covered by its support. In our method, the dictionary is a set of pre-defined rotation invariant binary patterns called as “rotation invariant binary Gabor patterns (BGP_ris)”. The occurrence histogram of BGP_ris can be formed to a given image. Then, the classification is based on the matching results between the sample histogram and the model histograms. The following figure shows the flowchart of our algorithm.

Evaluation Results

We conducted experiments on a modified CUReT database. It contains 61 textures and each texture has 92 images obtained under different viewpoints and illumination directions. The proposed BGP_ri was compared with the other five state-of-the-art rotation invariant texture representation methods, LBP [1], MR8 [2], Joint [3], BIF [4] and M-LBP [5]. In order to get statistically significant classification results, N training images were randomly chosen from each class while the remaining 92 – N  images per class were used as the test set. The partition was repeated 1000 times independently. The average accuracy along with one standard deviation for each method is reported in Table 1. In addition to the classification accuracy, we also care about the feature size and the classification speed of each method. At the classification stage, the histogram of the test image will be built at first and then it will be matched to all the models generated from the training samples. In Table 2, we list the feature size (number of histogram bins), the time cost for one test histogram construction and the time cost for one matching at the classification stage by each method. All the algorithms were implemented with Matlab 2010b except that a C++ implemented kd-tree (encapsulated in a MEX function) was used in MR8 and Joint to accelerate the labeling process. Experiments were performed on a Dell Inspiron 530s PC with Intel 6550 processor and 2GB RAM.

Table 1. Classification results (%)

Table 2. Feature size and time cost (msec)

Based on Table 1 and Table 2, we can have the following findings. First of all, BGP_ri can achieve higher classification accuracy than all the other methods evaluated, especially in the case of less training samples. Secondly, the proposed BGP_ri scheme requires a moderate feature size, a little bigger than LBP but much smaller than MR8, Joint, BIF, and M-LBP. The numbers of histogram bins for MR8, Joint, BIF, and M-LBP are 2400, 2400, 1296, and 540, while BGP_ri only needs 216 bins. Although the feature size of BGP_ri is a little bigger than LBP, considering the significant gain in the classification accuracy, it is deserved. Thirdly, these six schemes have quite different classification speeds. LBP runs fastest while BGP_ri ranks the second. Especially, BGP_ri works much faster than the two clustering based methods, MR8 and Joint. BGP_ri is nearly 40 times faster than MR8 and 100 times faster than Joint. In MR8 and Joint, to build the histogram of the test image, every pixel on the test image needs to be labeled to one item in the texton dictionary, which is quite time consuming. Such a process is not required in LBP, M-LBP, BIF, and BGP_ri. Besides, an extra training period is needed in MR8 and Joint to build the texton dictionary, which is also not required in LBP, M-LBP, GIF, and BGP_ri.

Reference                

[1] T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. PAMI, vol. 24, pp. 971-987, 2002.

[2] M. Varma and A. Zisserman, “A statistical approach to texture classification from single images,” Int. J. Comput. Vis., vol. 62, pp. 61-81, 2005.
[3] M. Varma and A. Zisserman, “A statistical approach to material classification using image patch exemplars,” IEEE Trans. PAMI, vol. 31, pp. 2032-2047, 2009.

[4] M. Crosier and L.D. Griffin, “Using basic image features for texture classification,” Int. J. Comput. Vis., vol. 88, pp. 447-460, 2010.

[5] L. Zhang, L. Zhang, Z. Guo, and D. Zhang, “Monogenic-LBP: a new approach for rotation invariant texture classification,” ICIP’10, pp. 2677-2680, 2010.

Created on: Nov. 29, 2012

Last update: Nov. 29, 2012