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• W2169657430 abstract "Synthetic aperture radar (SAR) image classification involves two crucial issues: suitable feature representation technique and effective pattern classification methodology. Here, we concentrate on the first issue. By exploiting a famous image feature processing strategy, Bag-of-Visual-Words (BOV) in image semantic analysis and the artificial immune systems (AIS)'s abilities of learning and adaptability to solve complicated problems, we present a novel and effective image representation method for SAR image classification. In BOV, an effective fused feature sets for local feature representation are first formulated, which are viewed as the low-level features in it. After that, clonal selection algorithm (CSA) in AIS is introduced to optimize the prediction error of k-fold cross-validation for getting more suitable visual words from the low-level features. Finally, the BOV features are represented by the learned visual words for subsequent pattern classification. Compared with the other four algorithms, the proposed algorithm obtains more satisfactory and cogent classification experimental results." @default.
• W2169657430 created "2016-06-24" @default.
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• W2169657430 date "2011-07-01" @default.
• W2169657430 modified "2023-09-25" @default.
• W2169657430 title "Bag-of-Visual-Words Based on Clonal Selection Algorithm for SAR Image Classification" @default.
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• W2169657430 doi "https://doi.org/10.1109/lgrs.2010.2100363" @default.
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