Local ternary pattern texture feature extraction method based on mean sampling

Abstract

The invention discloses a local ternary pattern texture feature extraction method based on mean sampling. First, the dimension of a local ternary coding sequence of all possible values is reduced preliminarily based on rotation invariance and is further reduced according to the dimension reduction conditions, and a pattern mapping table containing the corresponding relationship between original pattern number and final pattern number is built. Neighborhood circular symmetry average sampling is carried out on each non-edge pixel of a texture image of which the features are to be extracted, effective coding points are acquired from sampling points and coded to get a local ternary coding sequence of the non-edge pixels and corresponding original pattern numbers, corresponding final patterns are looked for in the pattern mapping table, the non-edge pixels covered by each final pattern are counted in the texture image, and a feature vector of the texture image is constructed. According to the invention, the noise is controlled and the accuracy of texture features is improved through mean sampling, and the dimension of the feature vector is controlled effectively through a dimension reduction method.

Description

technical field [0001] The invention belongs to the technical field of texture feature extraction, and more specifically relates to a local ternary pattern texture feature extraction method based on mean value sampling. Background technique [0002] The extraction of visual features is an important link in the process of image classification and recognition, and the quality of features directly affects the performance of the entire visual system. In long-term research, scholars have proposed various features to describe specific classification objects, and texture features are one of the important statistical features. It reflects the distribution of the texture pixel structure space in the grayscale image, and is generally the repeated reproduction of a series of texture primitives in the pixel space according to certain arrangement rules. The research on texture feature expression method has great practical significance for content-based image retrieval, remote sensing im...

AI Technical Summary

Problems solved by technology

However, this three-value pattern is not strictly a three-value pattern, and its final encoding is still binary. Similar LTP variants include high-order LTP (high-order LTP) proposed by ZhangY et al. These encoding methods It can only be called "pseudo-ternary mode"

The OLTP proposed by RajaM et al., and the OS-LTP proposed by HuangM et al. all use true three-value mode coding, but they are not enough in terms of rotation invariance and feature dimension, which are exactly what texture features must have. key elements of

[0006] The main disadvantage of the current local ternary pattern texture feature extraction method is that the dimension of the generated feature vector is too high to adapt to more than 16 neighborhood sampling points

Images