Taking Fuzzy-Rough Application to Mars
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Efficient entropy-based features selection for image retrieval
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Defect detection of IC wafer based on two-dimension wavelet transform
Microelectronics Journal
Wavelet based seam carving for content-aware image resizing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Automatic bioindicator images evaluation
INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
A new algorithm based on complex wavelet transform for protein sequence classification
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Fuzzy-rough feature selection aided support vector machines for Mars image classification
Computer Vision and Image Understanding
Fast image classification algorithms based on random weights networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Energy distribution over wavelet subbands is a widely used feature for wavelet packet based texture classification. Due to the overcomplete nature of the wavelet packet decomposition, feature selection is usually applied for a better classification accuracy and a compact feature representation. The majority of wavelet feature selection algorithms conduct feature selection based on the evaluation of each subband separately, which implicitly assumes that the wavelet features from different subbands are independent. In this paper, the dependence between features from different subbands is investigated theoretically and simulated for a given image model. Based on the analysis and simulation, a wavelet feature selection algorithm based on statistical dependence is proposed. This algorithm is further improved by combining the dependence between wavelet feature and the evaluation of individual feature component. Experimental results show the effectiveness of the proposed algorithms in incorporating dependence into wavelet feature selection.