ELMNET: FEATURE LEARNING USING EXTREME LEARNING MACHINES

被引:0
作者
Cui, Dongshun [1 ]
Huang, Guang-Bin [1 ]
Kasun, L. L. Chamara [1 ]
Zhang, Guanghao [1 ]
Han, Wei [1 ]
机构
[1] Nanyang Technol Univ, 50 Nanyang Ave, Singapore 639798, Singapore
来源
2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2017年
关键词
ELMNet; Feature Learning; ELM-AE; REPRESENTATION; RECOGNITION;
D O I
暂无
中图分类号
TB8 [摄影技术];
学科分类号
0804 ;
摘要
Feature learning is an initial step applied to computer vision tasks and is broadly categorized as: 1) deep feature learning; 2) shallow feature learning. In this paper we focus on shallow feature learning as these algorithms require less computational resources than deep feature learning algorithms. In this paper we propose a shallow feature learning algorithm referred to as Extreme Learning Machine Network (ELMNet). ELMNet is module based neural network consist of feature learning module and a post-processing module. Each feature learning module in ELMNet performs the following operations: 1) patch-based mean removal; 2) ELM auto-encoder (ELM-AE) to learn features. Post-processing module is inserted after the feature learning module and simplifies the features learn by the feature learning modules by hashing and block-wise histogram. Proposed ELMNet outperforms shallow feature learning algorithm PCANet on the MNIST hand-written dataset.
引用
收藏
页码:1857 / 1861
页数:5
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