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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jie Chen Ruiping Wang Shiguang Shan Xilin Chen Wen Gao |
| Copyright Year | 2006 |
| Description | Author affiliation: Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol. (Jie Chen) |
| Abstract | Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images - the image Euclidean distance based isomap (imisomap), in which we use a new distance for images called image Euclidean distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on Euclidean distance |
| Sponsorship | IEEE CPS |
| Starting Page | 1110 |
| Ending Page | 1113 |
| File Size | 220782 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769525210 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2006.729 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-08-20 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Euclidean distance Humans Noise robustness Noise level Pixel Computer science Research and development Face recognition Learning systems Laplace equations |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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