Handbook of Pattern Recognition and Computer Vision
Handbook of Pattern Recognition and Computer Vision
Texture classification using wavelet transform
Pattern Recognition Letters
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
A combined forecasting approach based on fuzzy soft sets
Journal of Computational and Applied Mathematics
Soft matrix theory and its decision making
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Text categorization based on fuzzy soft set theory
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part IV
Another approach to soft rough sets
Knowledge-Based Systems
FSSC: An Algorithm for Classifying Numerical Data Using Fuzzy Soft Set Theory
International Journal of Fuzzy System Applications
Entropy on intuitionistic fuzzy soft sets and on interval-valued fuzzy soft sets
Information Sciences: an International Journal
Intuitionistic fuzzy soft set theory and its decision making
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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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.