Name
Abstract | ||
|---|---|---|
Computer vision is one of the most important branches in modern industrial technology. Image classification plays an important role in computer vision since it utilizes the most advance technique in this area. However, most image classification methods only use the SIFT feature for further processing, which hinders the rich useful low-level image attributes to be captured. This paper proposes a maximal margin feature mapping framework that incorporates basic descriptors in the recognition system. This is fulfilled by optimizing an objective function that minimizes intra-class distance and maximizes interclass distance as well as the reconstruction error. An efficient optimization algorithm is proposed to learn the transformation matrix. Experiments on three publicly available datasets are conducted. The preliminary results show the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICIT.2016.7474849 | PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT) |
Keywords | Field | DocType |
image classification, maximal margin, feature mapping, non-convex optimization | Computer vision,Automatic image annotation,Bag-of-words model in computer vision,Feature detection (computer vision),Pattern recognition,Feature (computer vision),Image processing,Feature extraction,Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Contextual image classification,Mathematics | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changchen Zhao | 1 | 10 | 3.33 |
| Chun-Liang Lin | 2 | 245 | 37.49 |
| Weihai Chen | 3 | 4 | 3.45 |