Research on Texture Classification Based on Multi-Scale Information Fusion

Abstract

Texture feature is an important visual cue for an image, which is the unified description of human visual attributes and sensory attributes. The inherent problem of texture image is that the difference of intra-class images is large and the disparity of inter-class images is small. This problem increases the difficulty of texture image recognition. Therefore, improving the relevance embedding of intra-class images can reduce the classification errors caused by this problem. To solve this problem, this paper proposes a multi-scale information fusion network algorithm, which adopts a cascade structure. It combines multi-scale feature information with the corresponding background information. The shallow background information guides the next stage of feature formation and enhances the similarity of intra-class images. The intra-class feature information obtained is more general. The algorithm has been tested on data sets describable texture database (DTD) and Flickr material dataset (FMD), which has achieved good results.