Texture analysis using deterministic partially self-avoiding walk with thresholds

TL;DR

This paper introduces a multi-scale texture analysis method using deterministic partially self-avoiding walks on thresholded maps, demonstrating improved discrimination on the Brodatz database compared to traditional methods.

Contribution

The paper presents a novel multi-scale texture analysis technique based on deterministic walks on thresholded maps, enhancing texture discrimination capabilities.

Findings

Effective texture discrimination on Brodatz database

Outperforms traditional texture analysis methods

Multi-scale analysis captures diverse image properties

Abstract

In this paper, we propose a new texture analysis method using the deterministic partially self-avoiding walk performed on maps modified with thresholds. In this method, two pixels of the map are neighbors if the Euclidean distance between them is less than $\sqrt{2}$ and the weight (difference between its intensities) is less than a given threshold. The maps obtained by using different thresholds highlight several properties of the image that are extracted by the deterministic walk. To compose the feature vector, deterministic walks are performed with different thresholds and its statistics are concatenated. Thus, this approach can be considered as a multi-scale analysis. We validate our method on the Brodatz database, which is very well known public image database and widely used by texture analysis methods. Experimental results indicate that the proposed method presents a good texture discrimination, overcoming traditional texture methods.