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| Content Provider | ACM Digital Library |
|---|---|
| Author | He, Xiaofei |
| Abstract | Subspace learning techniques are widespread in pattern recognition research. They include Principal Component Analysis (PCA), Locality Preserving Projection (LPP), etc. These techniques are generally unsupervised which allows them to model data in the absence of labels or categories. In relevance feedback driven image retrieval system, the user provided information can be used to better describe the intrinsic semantic relationships between images. In this paper, we propose a semi-supervised subspace learning algorithm which incrementally learns an adaptive subspace by preserving the semantic structure of the image space, based on user interactions in a relevance feedback driven query-by-example system. Our algorithm is capable of accumulating knowledge from users, which could result in new feature representations for images in the database so that the system's future retrieval performance can be enhanced. Experiments on a large collection of images have shown the effectiveness and efficiency of our proposed algorithm. |
| Starting Page | 2 |
| Ending Page | 8 |
| Page Count | 7 |
| File Format | |
| ISBN | 1581138938 |
| DOI | 10.1145/1027527.1027530 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2004-10-10 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Linear discriminat analysis Relevance feedback Locality preserving projections Image retrieval Subspace learning Principal component analysis |
| Content Type | Text |
| Resource Type | Article |
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