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Title: mCENTRIST : a multi-channel feature generation
Authors: Xiao, Yang
Wu, Jianxin
Yuan, Junsong
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2014
Source: Xiao, Y., Wu, J., & Yuan, J. (2014). mCENTRIST: A Multi-Channel Feature Generation Mechanism for Scene Categorization. IEEE Transactions on Image Processing, 23(2), 823-836.
Series/Report no.: IEEE transactions on image processing
Abstract: mCENTRIST, a new multi-channel 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 multi-channel 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 hyper opponent color space is proposed by embedding Sobel information into the opponent color space for further performance improvements. Experiments show that mCENTRIST outperforms established multi-channel descriptors on four RGB and RGB-NIR datasets, including aerial orthoimagery, indoor and outdoor scene category recognition tasks. Experiments also verify that the hyper opponent color space enhances descriptors’ performance effectively.
ISSN: 1057-7149
DOI: 10.1109/TIP.2013.2295756
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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