On Exploring Multiplicity of Primitives and Attributes for Texture Recognition in the Wild.

Abstract

Texture recognition is a challenging visual task since its multiple primitives or attributes can be perceived from the texture image under different spatial contexts. Existing approaches predominantly built upon CNN incorporate rich local descriptors with orderless aggregation to capture invariance to the spatial layout. However, these methods ignore the inherent structure relation organized by primitives and the semantic concept described by attributes, which are critical cues for texture representation. In this paper, we propose a novel Multiple Primitives and Attributes Perception network (MPAP) that extracts features by modeling the relation of bottom-up structure and top-down attribute in a multi-branch unified framework. A bottom-up process is first proposed to capture the inherent relation of various primitive structures by leveraging structure dependency and spatial order information. Then, a top-down process is introduced to model the latent relation of multiple attributes by transferring attribute-related features between adjacent branches. Moreover, an augmentation module is devised to bridge the gap between high-level attributes and low-level structure features. MPAP can learn representation through jointing bottom-up and top-down processes in a mutually reinforced manner. Experimental results on six challenging texture datasets demonstrate the superiority of MPAP over state-of-the-art methods in terms of accuracy, robustness, and efficiency.