Texture Classification Using a Novel, Soft-Set Theory Based Classification Algorithm
In this paper, we have presented a new algorithm for classification of the natural textures. The proposed classification algorithm is based on the notions of soft set theory. The soft-set theory was proposed by D. Molodtsov which deals with the uncertainties. The choice of convenient parameterization strategies such as real numbers, functions, and mappings makes soft-set theory very convenient and practicable for decision making applications. This has motivated us to use soft set theory for classification of the textures. The proposed algorithm has very low computational complexity when compared with Bayes classification technique and also yields very good classification accuracy. For feature extraction, the textures are decomposed using standard dyadic wavelets. The feature vector is obtained by calculating averaged L1-norm energy of each decomposed channel. The database consists of 25 texture classes selected from Bordatz texture Album. Experimental results show the superiority of the proposed approach compared with some existing methods.
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