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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hongyu Li Rongjie Shi Wenbin Chen I-Fan Shen |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Comput. Sci. & Eng., Fudan Univ., Shanghai (Hongyu Li; Rongjie Shi) |
| Abstract | Image tangent space is actually high-level semantic space learned from low-level feature space by modified local tangent space alignment which was originally proposed for nonlinear manifold learning. Under the assumption that a data point in image space can be linearly approximated by some nearest neighbors in its local neighborhood, we develop a lazy learning method to locally approximate the optimal mapping function between image space and image tangent space. That is, the semantics of a new query image in image space can be inferred by the local approximation in its corresponding image tangent space. While Euclidean distance induced by the ambient space is often used to represent the difference between images, clearly, their natural distance is possibly different from Euclidean distance. Here, we compare three distance metrics: Chebyshev, Manhattan and Euclidean distances, and find that Chebyshev distance outperforms the other two in measuring the semantic similarity during retrieval. Experimental results show that our approach is effective in improving the performance of image retrieval systems |
| Sponsorship | IEEE CPS |
| Starting Page | 1126 |
| Ending Page | 1130 |
| File Size | 108922 |
| Page Count | 5 |
| File Format | |
| ISBN | 0769525210 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2006.690 |
| 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 | Image retrieval Chebyshev approximation Nearest neighbor searches Learning systems Euclidean distance Content based retrieval Computer science Mathematics Manifolds Linear approximation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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