Unsupervised Feature Learning for RGB-D Image Classification
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- 作者：I-Hong Jhuo (17)
Shenghua Gao (18)
Liansheng Zhuang (19)
D. T. Lee (17) (20)
Yi Ma (18) (21)
17. Institute of Information Science ; Academia Sinca ; Taipei ; Taiwan
18. School of Information Science and Technology ; ShanghaiTech University ; Shanghai ; China
19. CAS Key Laboratory of Electromagnetic Space Information ; USTC ; Hefei ; China
20. Department of Computer Science ; National Chung Hsing University ; Taichung ; Taiwan
21. Department of ECE ; University of Illinois at Urbana-Champaign ; Champaign ; USA - 刊名：Lecture Notes in Computer Science
- 出版年：2015
- 出版时间：2015
- 年：2015
- 卷：9003
- 期：1
- 页码：276-289
- 全文大小：1,037 KB
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- 丛书名：978-3-319-16864-7
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
文摘
Motivated by the success of Deep Neural Networks in computer vision, we propose a deep Regularized Reconstruction Independent Component Analysis network (R \(^2\) ICA) for RGB-D image classification. In each layer of this network, we include a R \(^2\) ICA as the basic building block to determine the relationship between the gray-scale and depth images corresponding to the same object or scene. Implementing commonly used local contrast normalization and spatial pooling, we gradually enhance our network to be resilient to local variance resulting in a robust image representation for RGB-D image classification. Moreover, compared with conventional handcrafted feature-based RGB-D image representation, the proposed deep R \(^2\) ICA is a feedforward network. Hence, it is more efficient for image representation. Experimental results on three publicly available RGB-D datasets demonstrate that the proposed method consistently outperforms the state-of-the-art conventional, manually designed RGB-D image representation confirming its effectiveness for RGB-D image classification.