Continuous rotation invariant local descriptors for texton dictionary-based texture classification

Texton dictionary-based texture representation approaches have been proven to be effective for texture classification. We propose two types of local descriptors based on Gaussian derivatives filters, both of them have the property of continuous rotation invariance. The first descriptor directly uses the maximum of the filter responses named continuous maximum responses (CMR). The second descriptor rectifies the filter responses to calculate principal curvatures (PC) of the image surface. The texton dictionary is learned from the training images by clustering the local descriptors, and the representation of each image is the frequency histogram of the textons. The classification results compared with some other popular methods on the CUReT, KTH-TIPS and KTH-TIPS2-a datasets show that representation based on CMR achieves best classification result on the CUReT dataset. The representation based on PC achieves the best classification results on the KTH-TIPS and KTH-TIPS2-a datasets, and the classification performance is robust on different datasets. The experiments of rotation invariant analysis implemented on the Brodatz album illustrate that the CMR descriptor has good inter-class distinguish ability and PC descriptor has strong intra-class congregate ability. The results demonstrate that the proposed local descriptors achieve remarkable performance to classify the rotated textures.

► Two continuous rotation invariant local descriptors are proposed.
► One descriptor is based on the maximum responses of the Gaussian derivatives filters.
► Another descriptor is based on the principal curvatures of the image surface.
► Both descriptors demonstrate their superiority to classify the rotated textures.