Texture-Based Features for Clothing Classification via Graph-Based Representation

Abstract

This paper proposes texture-based features for clothing category classification based on graph representation. Recently, graph-based representation has been used for texture characterization to aid texture analysis. In this work, graph-based theory is applied to characterize the local image structure. The rotation invariance uniformity (riu2) of local binary pattern mapping is adopted to represent feature descriptors. The proposed approach is evaluated by using the Brodatz and UIUC standard texture databases, and a clothing dataset. The proposed method is shown to be more effective for clothing classification than other methods.