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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - A cascade of deep learning fuzzy rule-based image classifier and SVM
AU - Angelov, Plamen Parvanov
AU - Gu, Xiaowei
N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2017/10/5
Y1 - 2017/10/5
N2 - In this paper, a fast, transparent, self-evolving, deep learning fuzzy rule-based (DLFRB) image classifier is proposed. This new classifier is a cascade of the recently introduced DLFRB classifier and a SVM based auxiliary. The DLFRB classifier serves as the main engine and can identify a number of human interpretable fuzzy rules through a very short, transparent, highly parallelizable training process. The SVM based auxiliary plays the role as a conflict resolver when the DLFRB classifier produces two highly confident labels for a single image. Only the fundamental image transformation techniques (rotation, scaling and segmentation) and feature descriptors (GIST and HOG) are used for pre-processing and feature extraction, but the proposed approach significantly outperforms the state-of-art methods in terms of both time and precision. Numerical experiments based on a handwriting digits recognition problem are used to demonstrate the highly accurate and repeatable performance of the proposed approach after a very shorting training process.
AB - In this paper, a fast, transparent, self-evolving, deep learning fuzzy rule-based (DLFRB) image classifier is proposed. This new classifier is a cascade of the recently introduced DLFRB classifier and a SVM based auxiliary. The DLFRB classifier serves as the main engine and can identify a number of human interpretable fuzzy rules through a very short, transparent, highly parallelizable training process. The SVM based auxiliary plays the role as a conflict resolver when the DLFRB classifier produces two highly confident labels for a single image. Only the fundamental image transformation techniques (rotation, scaling and segmentation) and feature descriptors (GIST and HOG) are used for pre-processing and feature extraction, but the proposed approach significantly outperforms the state-of-art methods in terms of both time and precision. Numerical experiments based on a handwriting digits recognition problem are used to demonstrate the highly accurate and repeatable performance of the proposed approach after a very shorting training process.
KW - deep learning
KW - cascade classifiers
KW - fuzzy rule-based classifier
KW - SVM
KW - handwritten digits recognition
U2 - 10.1109/SMC.2017.8122697
DO - 10.1109/SMC.2017.8122697
M3 - Conference contribution/Paper
SN - 9781538616468
SP - 746
EP - 751
BT - Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference on
PB - IEEE
T2 - IEEE SMC conference
Y2 - 5 October 2017
ER -