Heterogeneous patterns enhancing static and dynamic texture classification

TL;DR

This paper introduces a novel multi-scale method for classifying heterogeneous textures, improving accuracy over traditional global and texton-based approaches in static and dynamic textures.

Contribution

The paper proposes a new multi-scale pattern identification method that captures heterogeneous textures more effectively than existing global or texton-based techniques.

Findings

Improved classification accuracy on four static texture datasets.

Enhanced dynamic texture classification performance.

Outperforms conventional methods in heterogeneous texture analysis.

Abstract

Some mixtures, such as colloids like milk, blood, and gelatin, have homogeneous appearance when viewed with the naked eye, however, to observe them at the nanoscale is possible to understand the heterogeneity of its components. The same phenomenon can occur in pattern recognition in which it is possible to see heterogeneous patterns in texture images. However, current methods of texture analysis can not adequately describe such heterogeneous patterns. Common methods used by researchers analyse the image information in a global way, taking all its features in an integrated manner. Furthermore, multi-scale analysis verifies the patterns at different scales, but still preserving the homogeneous analysis. On the other hand various methods use textons to represent the texture, breaking texture down into its smallest unit. To tackle this problem, we propose a method to identify texture patterns not small as textons at distinct scales enhancing the separability among different types of texture. We find sub patterns of texture according to the scale and then group similar patterns for a more refined analysis. Tests were performed in four static texture databases and one dynamic one. Results show that our method provides better classification rate compared with conventional approaches both in static and in dynamic texture.