-Texture classification is a major issue in image analysis and pattern recognition. A number of methods are proposed in the literature including Local Binary Pattern (LBP). The LBP variant (s) plays an active role to extract texture features for texture classification. These are rotation invariant, noise sensitive or noise insensitive mehods. Each method has its own advantages and disadvantages...

Image retrieval is one of the main topics in the field of computer vision and pattern recognition. Local descriptors are gaining more and more recognition in recent years as these descriptors are capable enough to identify the unique features, which suitably and uniquely describe any image for recognition and retrieval. One of the popular and efficient frame works for capturing texture informat...

To realize quick and robust fire detection with image information of real scenes, smoke is a key feature information in detection methods. Since smoke does not keep stationary shape, it is difficult apply ordinal image processing techniques such as the edge or contour detection directly. Image information of smoke is also affected from its environmental conditions such as illumination changes a...

LBP (Local Binary Pattern) as an image operator is used to extract LBPH (LBP histogram) features for texture description. In this paper, we present a novel method to use LBPH feature in ordinary binary classification problem. Given a restricted local patch, the Chi square distance between the extracted LBPH and a reference histogram is used as a measure of confidence belonging to the reference ...

Local Binary Pattern is the most popular texture classification feature. It also shows excellent face detection performance. It is robust to the illumination and contrast variations as it considers the signs of the pixel differences. A Histogramming Local Binary Pattern code makes the descriptor resistant to translations within the Histogramming neighborhood. The two sets of novel edge-texture ...