Fast deterministic tourist walk for texture analysis
This addresses runtime efficiency for texture analysis in computer vision, but it is incremental as it optimizes an existing method.
The paper tackled the high runtime of deterministic tourist walk (DTW) for texture analysis by reducing the number of initial points used, showing that using fewer pixels significantly improves runtime with only a small decrease in classification accuracy on Brodatz and Vistex datasets.
Deterministic tourist walk (DTW) has attracted increasing interest in computer vision. In the last years, different methods for analysis of dynamic and static textures were proposed. So far, all works based on the DTW for texture analysis use all image pixels as initial point of a walk. However, this requires much runtime. In this paper, we conducted a study to verify the performance of the DTW method according to the number of initial points to start a walk. The proposed method assigns a unique code to each image pixel, then, the pixels whose code is not divisible by a given $k$ value are ignored as initial points of walks. Feature vectors were extracted and a classification process was performed for different percentages of initial points. Experimental results on the Brodatz and Vistex datasets indicate that to use fewer pixels as initial points significantly improves the runtime compared to use all image pixels. In addition, the correct classification rate decreases very little.