Texture characterization is a key problem in image understanding and pattern recognition. In this paper, we present a flexible shape-based texture representation using shape co-occurrence patterns. More precisely, texture images are first represented by a tree of shapes, each of which is associated with several geometrical and radiometric attributes. Then, four typical kinds of shape co-occurrence patterns based on the hierarchical relationships among the shapes in the tree are learned as codewords. Three different coding methods are investigated for learning the codewords, which can be used to encode any given texture image into a descriptive vector. In contrast with existing works, the proposed approach not only inherits the shape-based method’s strong ability to capture geometrical aspects of textures and high robustness to variations in imaging conditions but also provides a flexible way to consider shape relationships and to compute high-order statistics on the tree. To the best of our knowledge, this is the first time that co-occurrence patterns of explicit shapes have been used as a tool for texture analysis. Experiments on various texture and scene data sets demonstrate the efficiency of the proposed approach.
Over the past decades, tremendous investigations have been made in texture analysis, see e.g. @cite_30 @cite_3 @cite_37 @cite_46 @cite_0 @cite_13 @cite_1 @cite_2 , among which an active topic is developing texture models which can efficiently depict both the statistical and the geometrical aspects of textures and are robust to the variations of imaging condition as well.
@cite_30 @cite_37 @cite_1 @cite_3 @cite_0 @cite_2 @cite_46 @cite_13
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In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives. We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We develop an efficient algorithm for synthesizing random images subject to these constraints, by iteratively projecting onto the set of images satisfying each constraint, and we use this to test the perceptual validity of the model. In particular, we demonstrate the necessity of subgroups of the parameter set by showing examples of texture synthesis that fail when those parameters are removed from the set. We also demonstrate the power of our model by successfully synthesizing examples drawn from a diverse collection of artificial and natural textures. Based on multifractal analysis in wavelet pyramids of texture images, a new texture descriptor is proposed in this paper that implicitly combines information from both spatial and frequency domains. Beyond the traditional wavelet transform, a multi-oriented wavelet leader pyramid is used in our approach that robustly encodes the multi-scale information of texture edgels. Moreover, the resulting texture model shows empirically a strong power law relationship for nature textures, which can be characterized well by multifractal analysis. Combined with a statistics on affine invariant local patches, our proposed texture descriptor is robust to scale and rotation changes, more general geometrical transforms and illumination variations. In addition, the proposed texture descriptor is computationally efficient since it does not require many expensive processing steps, e.g., texton generation and cross-bin comparisons, which are often used by existing methods. As an application, the proposed descriptor is applied to texture classification and the experimental results on several public texture datasets verified the accuracy and efficiency of our descriptor. A texture descriptor based on the shape index and the accompanying curvedness measure is proposed, and it is evaluated for the automated analysis of astronomical image data. A representative sample of images of low-red shift galaxies from the Sloan Digital Sky Survey (SDSS) serves as a test bed. The goal of applying texture descriptors to these data is to extract novel information about galaxies, information which is often lost in more traditional analysis. In this study, we build a regression model for predicting a spectroscopic quantity, the specific star-formation rate (sSFR). As texture features we consider multi-scale gradient orientation histograms as well as multi-scale shape index histograms, which lead to a new descriptor. Our results show that we can successfully predict spectroscopic quantities from the texture in optical multi-band images. We successfully recover the observed bi-modal distribution of galaxies into quiescent and star-forming. The state-of-the-art for predicting the sSFR is a color-based physical model. We significantly improve its accuracy by augmenting the model with texture information. This study is the first step towards enabling the quantification of physical galaxy properties from imaging data alone.