Yong Wee Sek

Work place: Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

E-mail: ywsek@utem.edu.my


Research Interests: Multimedia Information System, Information Storage Systems, Information Systems, Computer Architecture and Organization, Computational Learning Theory, Computer systems and computational processes


YONG WEE SEK is a senior lecturer at the Department of Intelligent Computing and Analytics, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM). He completed his PhD in Business Information System in 2017 from RMIT University Melbourne, Australia. He received his Bachelor degree of Statistics at the Unversiti Kebangsaan Malaysia (UKM) and Master degree in Information Technology at the Universiti Putra Malaysia (UPM). His research interests involve operation research, information systems, web based and multimedia learning and mathematics. He is currently a member of the Computational Intelligence and Technologies Lab under the Centre for Advanced Computing Technology, UTeM.

Author Articles
Combining Multi-Feature Regions for Fine-Grained Image Recognition

By Sun Fayou Hea Choon Ngo Yong Wee Sek

DOI: https://doi.org/10.5815/ijigsp.2022.01.02, Pub. Date: 8 Feb. 2022

Fine-grained visual classification(FGVC) is challenging task duo to the subtle discriminative features.Recently, RA-CNN selects a single feature region of the image, and recursively learns the discriminative features. However, RA-CNN abandons most of feature regions, which is not only the inefficient but aslo ineffective.To address above issues,we design a noval fine-grained visual recognition model MRA-CNN,which associates multi-feature regions.To improve the feature representation,attention blocks are integrated into the backbone to reinforce significant features;To improve the classification accuracy, we design the feature scale dependent(FSD) algorithm to select the optimal outputs as the classifier inputs;To avoid missing features, we adopt the k-means algorithm to select multiple feature regions.We demonstrate the value of MRA-CNN by expensive experiments on three popular fine-grained benchmarks:CUB-200-2011,Cars196 and Aircrafts100 where we achieve state-of-the-art performance.Our codes can be found at https://github.com/dlearing/MRA-CNN.git.

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