Texture classification by wavelet packet signatures

Abstract

This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) reflected a specific scale and orientation sensitivity. Wavelet packet representations for twenty-five natural textures were classified without error by a simple two-layer network classifier. An analyzing function of large regularity (0 2 0) was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments (Ds). In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classification without error for the twenty-five textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture.

Tri-Chrominance Texture Pattern: A New Feature Descriptor

“…Various filters were described by Randen and Hussy [35] and RiveroMoreno and Bres [36]. The transformations comprise wavelets [15,34], wavelet packets [22] and curvelets [39]. Recent technological advances allow exploration of human perception of more elaborate techniques [7,8].…”

Granulometric analysis based on the energy of Wavelet Transform coefficients

“…In fact, the recent methods for texture classifi cation support the notion of spatial-frequency (multiscale) analysis that maximizes the simultaneous localization of energy in both spatial and frequency domains. Such analysis may be provided using the multiresolution representation of the wavelet theory (Laine & Fan, 1993;Unser, 1995). Experimentally, the wavelet's capabilities have provided a powerful discrimination tool despite their sensitivity and selectivity, compared to traditional resolution techniques (Aksoy & Haralick, 1998;Davis, 1980).…”

Correlated Networks for Content Based Image Retrieval

“…Common ground of all proposed CBIR systems is their intention to address image searching problem in a more effective way by addressing new feature types and image similarity detection measures. For this the focus of research is on texture [11,12,13,14], color [14,15], or shape [16] features or any of these combinations [13]. As far as the scope of features is concerned, features can be divided into two main categories: global features and local features.…”

“…Each of these bases offers a particular way of coding signals, reconstructing exact features and preserving global energy. The inverse relationship between wavelet packets of different scales can be shown through [11]: Equation (3) can be used to calculate the wavelet packets. Coefficients of coarser scale can be calculated using eq.…”

“…To analyze the signals in a rich way the wavelet packet method is used which is a generalization of wavelet decomposition and can be described by the collection of functions {WJ(x)|J Z+}obtained from [11]: Where 'p' is a scale index and 'l' is the translation index. W_0 x= Φ(x) is the scaling function.…”