Mobile based big data design patent image retrieval system via Lp norm deep learning approach
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4886-4889 |
| ISBN (Print) | 9781479917624 |
| Publication status | Published - Nov 2015 |
Conference
| Title | 41st Annual Conference of the IEEE Industrial Electronics Society, IECON 2015 |
|---|---|
| Place | Japan |
| City | Yokohama |
| Period | 9 - 12 November 2015 |
Link(s)
Abstract
This paper proposes a mobile based big data design patent image retrieval system via a deep learning approach. The images are represented via sparse vectors by a dictionary. The joint representation and dictionary design problem is formulated as a mixed L2 and Lp optimization problem. An iterative algorithm is employed for finding a locally optimal solution. Experimental results show that the retrieval accuracy is high.
Citation Format(s)
Mobile based big data design patent image retrieval system via Lp norm deep learning approach. / Su, Jing; Ling, Bingo W. K.; Dai, Qingyun et al.
IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society. Institute of Electrical and Electronics Engineers Inc., 2015. p. 4886-4889 7392866.
IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society. Institute of Electrical and Electronics Engineers Inc., 2015. p. 4886-4889 7392866.
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with host publication) › peer-review
