Food texture descriptors based on fractal and local gradient information

This work is motivated by the desire to use image analysis methods to identify and characterize images of food items to aid in dietary assessment. This paper introduces three texture descriptors for texture classification that can be used to classify images of food. Two are based on the multifractal analysis, namely, entropy-based categorization and fractal dimension estimation (EFD), and a Gabor-based image decomposition and fractal dimension estimation (GFD). Our third texture descriptor is based on the spatial relationship of gradient orientations (GOSDM), by obtaining the occurrence rate of pairs of gradient orientations at different neighborhood scales. The proposed methods are evaluated in texture classification and food categorization tasks using the entire Brodatz database and a customized food dataset with a wide variety of textures. Results show that for food categorization our methods consistently outperform several widely used techniques for both texture and object categorization.