Abstract
Image recognition with incomplete data is a well-known hard problem in multimedia content analysis. This paper proposes a novel deep learning technique called semiconducting bilinear deep belief networks (SBDBN) by referencing human's visual cortex and intelligent perception. Inheriting from deep models, SBDBN simulates the laminar structure of human's cerebral cortex and the neural loop in human's visual areas. To address the special difficulties of image recognition with incomplete data, we design a novel second-order deep architecture with semiconducting restricted boltzmann machines. Moreover, two peaks activation of human's perception is implemented by three learning stages of semiconducting bilinear discriminant initialization, greedy layer-wise reconstruction, and global finetuning. Owing to exploiting the embedding information according to the reliable features rather than any completion of missing features, the proposed SBDBN has demonstrated outstanding recognition ability on two standard datasets and one constructed dataset, comparing with both incomplete image recognition techniques and existing deep learning models.
Original language | English |
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Title of host publication | Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, ICMR 2012 |
DOIs | |
Publication status | Published - 27 Jul 2012 |
Event | 2nd ACM International Conference on Multimedia Retrieval, ICMR 2012 - Hong Kong, Hong Kong Duration: 5 Jun 2012 → 8 Jun 2012 |
Conference
Conference | 2nd ACM International Conference on Multimedia Retrieval, ICMR 2012 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 5/06/12 → 8/06/12 |
Keywords
- Deep learning
- Image recognition
- Missing features
- Semiconducting bilinear discriminant initialization
- Semiconducting RBM
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Software