A Novel Texture Descriptor Using Over-Complete Wavelet Transform and Its Fractal Signature

In the paper, we proposed a novel feature descriptor using over-complete wavelet transform and wavelet domain based fractal signature for texture image analysis and retrieval. Traditionally, discrete wavelet frame took the first order derivative of smoothing function into account, which is equivalent to Canny edge detection, with the specific case using Gaussian function as smoothing function. The second order derivative Spline Wavelet has more stronger ability to distinguish the variation of the edge width than the first order one. The over-complete B-Spline wavelet scheme is discussed and the finite impulse response of over-complete wavelet transform is also represented in the paper. In feature extraction phase, 56 dimensional statistical features, including means and variances in positive and negative parts of wavelet coefficients, are extracted respectively. At the same time, the fractal signature based on the fractal surface area function in a Besov space is very accurate and ...

• Type: Conference
• Year: 2005
• Where: CIVR
• Authors: Qing Wang, David Dagan Feng
• Keywords: CIVR 2005, Over-complete Wavelet, Over-complete Wavelet Transform, Wavelet