Ripplet-II transform for feature extraction

Jun Xu; Dapeng Wu

doi:10.1117/12.863013

4 August 2010 Ripplet-II transform for feature extraction

Jun Xu, Dapeng Wu

Proceedings Volume 7744, Visual Communications and Image Processing 2010; 77441R (2010) https://doi.org/10.1117/12.863013
Event: Visual Communications and Image Processing 2010, 2010, Huangshan, China

Abstract

Current image representation schemes have limited capability of representing 2D singularities (e.g., edges in an image). Wavelet transform has better performance in representing 1D singularities than Fourier transform. Recently invented ridgelet and curvelet transform achieve better performance in resolving 2D singularities than wavelet transform. To further improve the capability of representing 2D singularities, this paper proposes a new transform called ripplet transform Type II (ripplet-II). The new transform is able to capture 2D singularities along a family of curves in images. In fact, ridgelet transform is a special case of ripplet-II transform with degree 1. Ripplet-II transform can be used for feature extraction due to its efficiency in representing edges and textures. Experiments in texture classification and image retrieval demonstrate that the ripplet-II transform based scheme outperforms wavelet and ridgelet transform based approaches.

Citation Download Citation

Jun Xu and Dapeng Wu "Ripplet-II transform for feature extraction", Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77441R (4 August 2010); https://doi.org/10.1117/12.863013

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available