Texture descriptor combining fractal dimension and artificial crawlers
This work addresses texture description for image analysis applications, but it appears incremental as it builds on existing methods like artificial crawlers and fractal dimension.
The paper tackled the problem of texture analysis by proposing a new method that combines artificial crawlers with fractal dimension to capture image surface details, achieving highly discriminative features validated on two texture datasets.
Texture is an important visual attribute used to describe images. There are many methods available for texture analysis. However, they do not capture the details richness of the image surface. In this paper, we propose a new method to describe textures using the artificial crawler model. This model assumes that each agent can interact with the environment and each other. Since this swarm system alone does not achieve a good discrimination, we developed a new method to increase the discriminatory power of artificial crawlers, together with the fractal dimension theory. Here, we estimated the fractal dimension by the Bouligand-Minkowski method due to its precision in quantifying structural properties of images. We validate our method on two texture datasets and the experimental results reveal that our method leads to highly discriminative textural features. The results indicate that our method can be used in different texture applications.