Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Multiscale Fractal Characterization of Three-Dimensional Gene Expression Data
SIBGRAPI '02 Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing
Fractal dimension applied to plant identification
Information Sciences: an International Journal
Fractal features for localization of temporal lobe epileptic foci using SPECT imaging
Computers in Biology and Medicine
A comparative study on multiscale fractal dimension descriptors
Pattern Recognition Letters
Hi-index | 12.05 |
This work proposes a novel texture descriptor based on fractal theory. The method is based on the Bouligand-Minkowski descriptors. We decompose the original image recursively into four equal parts. In each recursion step, we estimate the average and the deviation of the Bouligand-Minkowski descriptors computed over each part. Thus, we extract entropy features from both average and deviation. The proposed descriptors are provided by concatenating such measures. The method is tested in a classification experiment under well known datasets, that is, Brodatz and Vistex. The results demonstrate that the novel technique achieves better results than classical and state-of-the-art texture descriptors, such as Local Binary Patterns, Gabor-wavelets and co-occurrence matrix.