Texture analysis using deterministic partially self-avoiding walk with thresholds
This work addresses texture analysis for image processing applications, but it appears incremental as it builds on existing walk-based methods with threshold modifications.
The paper tackles texture analysis by proposing a new method using deterministic partially self-avoiding walks on threshold-modified maps, which highlights image properties through multi-scale feature extraction. It validates the method on the Brodatz database, showing good texture discrimination and outperforming traditional methods.
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.