Texture Classification Using Sparse Frame-Based Representations
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Texture Classification Using Sparse Frame-Based Representations Karl Skretting and John H˚akon Husøy Department of Electrical and Computer Engineering, University of Stavanger, 4036 Stavanger, Norway Received 31 August 2004; Revised 20 April 2005; Accepted 2 June 2005 A new method for supervised texture classification, denoted by frame texture classification method (FTCM), is proposed. The method is based on a deterministic texture model in which a small image block, taken from a texture region, is modeled as a sparse linear combination of frame elements. FTCM has two phases. In the design phase a frame is trained for each texture class based on given texture example images. The design method is an iterative procedure in which the representation error, given a sparseness constraint, is minimized. In the classification phase each pixel in a test image is labeled by analyzing its spatial neighborhood. This block is represented by each of the frames designed for the texture classes under consideration, and the frame giving the best representation gives the class. The FTCM is applied to nine test images of natural textures commonly used in other texture classification work, yielding excellent overall performance. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
Most surfaces exhibit texture. For human beings it is quite easy to recognize different textures, but it is more difficult to precisely define a texture. Under all circumstances, a texture may be regarded as a region where some elements or primitives are repeated and arranged according to a placement rule. Tuceryan and Jain [1] list more possible definitions and give a comprehensive overview of texture classification. Possible applications can be grouped into (1) texture analysis, that is, finding some appropriate properties for a texture, (2) texture classification, that is, identifying the texture class in a homogeneous region, and (3) texture segmentation, that is, finding a boundary map between different texture regions of an image. The boundary map may be used for object recognition and scene interpretation in areas such as medical diagnostics, geophysical interpretation, industrial automation, and image indexing. Finally, (4) texture synthesis, that is, generating artificial textures to be used for example in computer graphics or image compression. Some examples of applications are presented in [2–6]. Typically, texture classification algorithms have two main parts: a local feature vector is found, which is subsequently used for texture classification or segmentation. The methods for feature extraction may be loosely grouped as statistical, geometrical, model-based, and signal processing (filtering) methods [1]. For the filtering methods the feature vectors are often built as variance estimates, local energy measures, for
each of the subbands of a filter bank. Also, there are numerous classification or pattern recognition methods available. The Bayes classifier is probably the most common one [7, 8]. The min- or max-selec
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