Adrien Depeursinge will be a speaker at the MIT CSAIL Seminar Series: Biomedical Imaging and Analysis 2015/2016. The topic of his talk is ‘Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification’. The presentation will take place Thursday, July 28, 2016.
SUMMARY
We present texture operators encoding class-specific local organizations of image directions (LOID) in a rotation-invariant fashion. The LOIDs are key for visual understanding, and are at the origin of the success of the popular approaches such as local binary patterns (LBP) and the scale-invariant feature transform (SIFT). Whereas LBPs and SIFT yield handcrafted image representations, we propose to learn data-specific representations of the LOIDs in a rotation-invariant fashion. The image operators are based on steerable circular harmonic wavelets (CHW), offering a rich and yet compact initial representation for characterizing natural textures. The joint location and orientation required to encode the LOIDs is preserved by using moving frames (MF) texture representations built from locally-steered multi-order CHWs. In a second step, we use support vector machines (SVM) to learn a multi-class shaping matrix of the initial CHW representation, yielding data-driven MFs that are invariant to rigid motions. We experimentally demonstrate the effectiveness of the proposed operators for classifying natural textures.