Innovative Texture Database Collecting Approach and Feature Extraction Method based on Combination of Gray Tone Difference Matrixes, Local Binary Patterns,and K-means Clustering
This work addresses a domain-specific issue for image processing researchers by providing an incremental improvement in texture database collection to reduce bias in evaluation.
The paper tackled the problem of biased texture classification performance due to reliance on popular databases by proposing a new approach to collect more efficient texture image databases using a combination of gray tone difference matrices, local binary patterns, and K-means clustering. The result showed improved performance with a computed Fisher rate for the collected database and competitive accuracy compared to state-of-the-art methods in texture classification.
Texture analysis and classification are some of the problems which have been paid much attention by image processing scientists since late 80s. If texture analysis is done accurately, it can be used in many cases such as object tracking, visual pattern recognition, and face recognition.Since now, so many methods are offered to solve this problem. Against their technical differences, all of them used same popular databases to evaluate their performance such asBrodatz or Outex, which may be made their performance biased on these databases. In this paper, an approach is proposed to collect more efficient databases of texture images. The proposed approach is included two stages. The first one is developing feature representation based on gray tone difference matrixes and local binary patterns features and the next one is consisted an innovative algorithm which is based on K-means clustering to collect images based on evaluated features. In order to evaluate the performance of the proposed approach, a texture database is collected and fisher rate is computed for collected one and well known databases. Also, texture classification is evaluated based on offered feature extraction and the accuracy is compared by some state of the art texture classification methods.