Title: Multi-model Feature Integration For Texture Classification
Abstract: The Wold texture model considers a textured pattern being composed of two main types of homogenous random fields, namely the deterministic and the indeterministic fields. The two fields can be represented by different type of models. It is known that, for textures, the multichannel model based on Gabor function(Gabor model) is very effective for representing the deterministic fields, and a Gaussian Markov Random Field(GMRF) model is very effective in representing the indeterministic fields. In this paper, we propose to use both models for texture classification, in which features based on each model are integrated according to the consensus theory. A weighting parameter, the deterministic energy ratio determined from the spectrum distribution function, is used as the flexible weight in the consensus theory. In this way, a wider variety of textures can be better-represented and hence lead to better classification of the textures. The classification problem is basically the problem of identifying an observed textured sample as one of several possible texture classes by a reliable but computationally attractive texture classifier. This implies that the choice of the textural features should be as compact as possible, and yet as discriminating as possible. In other words, the extraction of texture features should efficiently embody information about the textural characteristics of the image pattern. Early methods on texture classification were based on single model feature which reflected the statistical or structural properties of texture image. The statistical feature characterizes the texture by statistics of image pixel gray scale values. These methods give relatively high recognition rate when the test pattern has random looking texture, but not for large scale and more structured patterns. The structural feature assumes that the texture is generated by the placement of the primitives according to certain placement rules. These methods usually give good results in classifying structural textures[12][3]. Natural texture usually contains both structural and statistical properties. Single feature set is insufficient to describe texture image completely. Combining texture features has been suggested by some authors[10][11][9]. Conceptually, the simplest method is the stacked-vector approach in which multi-model features are concatenated together into a single feature vector and input to a classifier. This method is very straightforward and work well if the features are similar. However, the method is not applicable when the features cannot be described by a common model, e.g. the multivariate Gaussian model. In this paper, we proposed a texture classification method based on multi-model feature integration by consensus theory[1]. Two feature sets are extracted from the two components of the texture image decomposed from the image by the Wold-like decomposition[5]. Based on the consensus rule, two feature sets are combined with a flexible weight. 112 texture classes from Brodatz database[2] were used for analysis of classification performance.
Publication Year: 2002
Publication Date: 2002-01-01
Language: en
Type: article
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Cited By Count: 2
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