Description

Local binary patterns (LBP) is a type of feature used for classification in computer vision. It has since been found to be a powerful feature for texture classification; it has further been determined that when LBP is combined with the Histogram of oriented gradients (HOG) descriptor, it improves the detection performance considerably on some datasets. The local binary pattern operator is an image operator which transforms an image into an array or image of integer labels describing small-scale appearance of the image. These labels or their statistics, most commonly the histogram, are then used for further image analysis. The most widely used versions of the operator are designed for monochrome still images but it has been extended also for color (multi-channel) images as well as videos and volumetric data. Extracting efficient texture patterns will be helpful in the identification of best features from the images. The selection of the best features produces effective accuracy in classification process. A method that extracts efficient features from the images based on Sorted Consecutive Local Binary Patterns (SCLBP) is proposed. In the proposed SCLBP all the patterns of LBP were selected in a rotation invariant manner. Compared to the conventional LBP the patterns obtained using proposed method is unique. The extracted features were then classified using Support Vector machine (SVM) classifier. The performance of the process is measured on the basis of the Accuracy, Sensitivity and Specificity of the classifier.