Compact Local Directional Texture Pattern for Local Image Description

Texture Classification for Image Retrieval using Local Structure Pattern

Compact Description of Local Features

Considering the growing amount of digital images in all kind of databases, the search for specific content remains a problem. Content-based image retrieval based on local features is a promising approach but comes with the problem of being memory and computational intensive. The bag of keypoints approach reduces the feature vectors to one histogramm per image. This paper shows an efficient clus...

Local Derivative Pattern with Smart Thresholding: Local Composition Derivative Pattern for Palmprint Matching

Palmprint recognition is a new biometrics system based on physiological characteristics of the palmprint, which includes rich, stable, and unique features such as lines, points, and texture. Texture is one of the most important features extracted from low resolution images. In this paper, a new local descriptor, Local Composition Derivative Pattern (LCDP) is proposed to extract smartly stronger...

Compact color-texture description for texture classification

Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature....

Rotation and scale invariant local binary pattern based on high order directional derivatives for texture classification

Article history: Available online 18 December 2013