Abstract
Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor.
Highlights
Texture analysis is an active and fundamental research topic in the fields of computer vision and pattern recognition
To evaluate the effectiveness of the proposed methods, we carried out a series of experiments on three large and comprehensive texture databases: the Columbia-Utrecht Reflection and Texture (CUReT) database, which contains 61 classes of realworld textures, each imaged under different combinations of illumination and viewing angle [27], University of Illinois at Urbana-Champaign (UIUC) database [14], which includes 25 classes and 40 images per class collected under significant viewpoint variations, and Kungliga Tekniska hogskolan (Swedish) -Textures under varying Illumination, Pose and Scale (KTHTIPS) database [28], which include contains 10 classes and 81 images per class imaged under different scales, different poses and different illumination conditions
Experimental Results on CUReT Database The CURet database contains 61 textures, as shown in Fig. 10, and there are 205 images of each texture acquired at different viewpoints and illumination orientations
Summary
Texture analysis is an active and fundamental research topic in the fields of computer vision and pattern recognition. Statistical texton based methods are simple to implement and could achieve good performance on texture image classification [16,17,18,19,20,21,22]. The 38 bits is divided into 8 rows based on the filters shown, for each row, a rotation invariant sub-texton is defined through one scale of filter(s). For the first six rows, the output is 6-bits binary string, a rotation invariant sub-texton designed based on the idea of ‘‘rotation invariant uniform’’ [25] is defined: ST. Similar to Binary_MR8, for each position, the 38 binary bits of BDi(x,y)and BLi(x,y)are divided into 8 rows respectively and for each row a rotation invariant sub-texton is defined. The nearest neighborhood classifier with chi-square distance is used to measure the dissimilarity between two histograms, because it is equivalent to the optimal Bayesian classification [23], and good performance for texture classification can be achieved [24]
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