Biomedical Imaging GroupSTI
English only   BIG > Publications > Steerable Wavelets

 Home Page
 News & Events
 Tutorials and Reviews
 Download Algorithms

 All BibTeX References

Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification

A. Depeursinge, Z. Püspöki, J.P. Ward, M. Unser

IEEE Transactions on Image Processing, vol. 26, no. 4, pp. 1626-1636, April 2017.

We present texture operators encoding class-specific local organizations of image directions (LOIDs) 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 (LBPs) 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 (CHWs), 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 (MFs) texture representations built from locally-steered image gradients that are invariant to rigid motions. In a second step, we use support vector machines to learn a multi-class shaping matrix for the initial CHW representation, yielding data-driven MFs called steerable wavelet machines (SWMs). The SWM forward function is composed of linear operations (i.e., convolution and weighted combinations) interleaved with non-linear steermax operations. We experimentally demonstrate the effectiveness of the proposed operators for classifying natural textures. Our scheme outperforms recent approaches on several test suites of the Outex and the CUReT databases.

AUTHOR="Depeursinge, A. and P{\"{u}}sp{\"{o}}ki, Z. and Ward, J.P. and
        Unser, M.",
TITLE="Steerable Wavelet Machines ({SWM}): {L}earning Moving Frames for
        Texture Classification",
JOURNAL="{IEEE} Transactions on Image Processing",

© 2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.