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• W2071309849 abstract "mCENTRIST, a new multichannel feature generation mechanism for recognizing scene categories, is proposed in this paper. mCENTRIST explicitly captures the image properties that are encoded jointly by two image channels, which is different from popular multichannel descriptors. In order to avoid the curse of dimensionality, tradeoffs at both feature and channel levels have been executed to make mCENTRIST computationally practical. As a result, mCENTRIST is both efficient and easy to implement. In addition, a hyperopponent color space is proposed by embedding Sobel information into the opponent color space for further performance improvements. Experiments show that mCENTRIST outperforms established multichannel descriptors on four RGB and RGB-near infrared data sets, including aerial orthoimagery, indoor, and outdoor scene category recognition tasks. Experiments also verify that the hyper opponent color space enhances descriptors' performance effectively." @default.
• W2071309849 created "2016-06-24" @default.
• W2071309849 creator A5069201529 @default.
• W2071309849 creator A5085245110 @default.
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• W2071309849 date "2014-02-01" @default.
• W2071309849 modified "2023-10-07" @default.
• W2071309849 title "mCENTRIST: A Multi-Channel Feature Generation Mechanism for Scene Categorization" @default.
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• W2071309849 doi "https://doi.org/10.1109/tip.2013.2295756" @default.
• W2071309849 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26270921" @default.
• W2071309849 hasPublicationYear "2014" @default.
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