Chaimae Anibou, Mohammed Nabil Saidi, Driss Aboutajdine, Journal of Information Processing Systems Vol. 11, No. 3, pp. 421-437, Sep. 2015  

10.3745/JIPS.02.0028
Keywords: Discrete wavelet transform, feature extraction, Fuzzy Set Theory, Information Fusion, Probability Theory, Segmentation, supervised classification
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Abstract

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on infor- mation fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the per- formance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.

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