A Two-Stage Combined Classifier in Scale Space Texture Classification

TL;DR

This paper introduces a two-stage combined classifier approach for multiscale texture classification, leveraging scale-space theory and feature combination to improve accuracy on small image patches.

Contribution

It proposes a novel two-stage combined classifier method that effectively integrates multiscale features for texture analysis, outperforming traditional methods like SVM on small datasets.

Findings

Combining classifiers outperforms combining feature spaces for small samples.

The proposed method surpasses SVM in multiscale texture classification.

Two-stage combination enhances classification accuracy across scales.

Abstract

Textures often show multiscale properties and hence multiscale techniques are considered useful for texture analysis. Scale-space theory as a biologically motivated approach may be used to construct multiscale textures. In this paper various ways are studied to combine features on different scales for texture classification of small image patches. We use the N-jet of derivatives up to the second order at different scales to generate distinct pattern representations (DPR) of feature subsets. Each feature subset in the DPR is given to a base classifier (BC) of a two-stage combined classifier. The decisions made by these BCs are combined in two stages over scales and derivatives. Various combining systems and their significances and differences are discussed. The learning curves are used to evaluate the performances. We found for small sample sizes combining classifiers performs significantly better than combining feature spaces (CFS). It is also shown that combining classifiers performs better than the support vector machine on CFS in multiscale texture classification.