# Features

The local binary patterns method is a method used for image feature classification in computer vision. In September 1994, the local image processing method was first proposed by T. Ojala, M. pietik äinen, and D. Harwood for texture feature extraction. Later, the combination of the LDA method and the hog feature classifier improved the detection performance on some datasets [45.

The following describes how to extract a feature vector from a local string:

First, the detection window is divided into 16 × 16 small areas (cells). For one pixel in each cell, compare the values of the B points (or multiple points in the ring area, for example, the three examples of the neighboring areas using the KNN algorithm) in a clockwise or counterclockwise manner, if the center pixel value is greater than the neighboring point, the neighboring point is assigned 1; otherwise, the value is 0, so that each point will get an 8-bit binary number (usually converted to a decimal number ). Then, calculate the histogram of each cell, that is, the occurrence frequency of each number (assumed to be a decimal number) (that is, a binary sequence about whether each pixel is larger than a neighboring point ), then normalize the histogram. Finally, we connect the statistical histograms of each cell to obtain the feature of the whole image. Then we can use SVM or other machine learning algorithms for classification.

First, define a texture image, which is a function that varies with the following variables: texture surface material, reflectivity, illumination, camera and other angles. Currently, texture classification is popular in two ways: global features, such as HSV and Gabor, and local features, such as Harris-Laplace, the local feature-based method is mainly based on the texton framework, that is, the bag-of-words framework for image classification.

Today, we will first introduce Global-based features, which are the simplest and most effective. Next we will introduce a global feature and its variants.

1. HSV

The P-point neighbor with R as the radius, GC as the center, GP as the neighborhood, and the difference between the neighborhood and the center brightness is large or small.

Changes PR to form a multi-scale HSV

At present, I know this is enough. If you want to continue to understand it, see

Http://blog.csdn.net/djh512/article/details/9001518

 

# Features