The local binary pattern (LBP) provides a simple and efficient approach to gray-scale and rotation invariant texture classification. However, the LBP operator thresholds P neighbors at the value of the center pixel in a local neighborhood and employs a P-bit binary pattern to encode only the signs of the differences between the gray values. Thus, the LBP operator discards some important texture...

Diagnostic and classification algorithms play an important role in data analysis, with applications in areas such as health care, fault diagnostics, or benchmarking. Branching programs (BP) is a popular representation model for describing the underlying classification/diagnostics algorithms. Typical application scenarios involve a client who provides data and a service provider (server) whose d...

Recently, the local binary pattern (LBP) has been widely used in texture classification. The conventional LBP methods only describe micro structures of texture images, such as edges, corners, spots and so on, although many of them show a good performance on texture classification. This situation still could not be changed, even though the multiresolution analysis technique is used in methods of...

In this paper we propose a novel texture recognition feature called Fractal Weighted Local Binary Pattern (FWLBP). It has been observed that fractal dimension (FD) measure is relatively invariant to scale-changes, and presents a good correlation with human perception of surface roughness. We have utilized this property to construct a scale-invariant descriptor. We have sampled the input image u...

Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale in...

The statistical approaches such as texture spectrum and local binary pattern methods have been discussed in this paper. The features are extracted by the computation of LBP and Texture Spectrum histogram. A combined approach of LBP with texture spectrum is also proposed further. Experiments of texture feature extraction, classification of textures and similarity-based image-to-image matching ar...

A novel object tracking algorithm is presented in this paper by using the joint colortexture histogram to represent a target and then applying it to the mean shift framework. Apart from the conventional color histogram features, the texture features of the object are also extracted by using the local binary pattern (LBP) technique to represent the object. The major uniform LBP patterns are expl...

In this paper we present an approach to music genre classification which converts an audio signal into spectrograms and extracts texture features from these time-frequency images which are then used for modeling music genres in a classification system. The texture features are based on Local Binary Pattern, a structural texture operator that has been successful in recent image classification re...

Breast cancer is a frequent cancer diseases and it is the leading cause of cancer death among women in most of the occidental countries. Mammography is one among the key tool to identify the location and size of tumor in the breast. Texture analysis plays an important role in detecting the disease patterns in mammogram and to identify the masses as normal or abnormal. The local binary pattern d...