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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jianwei Luo Zhiguo Jiang |
| Copyright Year | 2014 |
| Description | Author affiliation: Image Process. Center, Beihang Univ., Beijing, China (Jianwei Luo; Zhiguo Jiang) |
| Abstract | This paper addresses the problem of learning semantic compact binary codes for efficient retrieval in large-scale image collections. Our contributions are three-fold. Firstly, we introduce semantic codes, of which each bit corresponds to an attribute that describes a property of an object (e.g. dogs have furry). Secondly, we propose to use matrix factorization (MF) to learn the semantic codes by encoding attributes. Unlike traditional PCA-based encoding methods which quantize data into orthogonal bases, MF assumes no constraints on bases, and this scheme is coincided with that attributes are correlated. Finally, to augment semantic codes, MF is extended to encode extra non-semantic codes to preserve similarity in origin data space. Evaluations on a-Pascal dataset show that our method is comparable to the state-of-the-art when using Euclidean distance as ground truth, and even outperforms state-of-the-art when using class label as ground truth. Furthermore, in experiments, our method can retrieve images that share the same semantic properties with the query image, which can be used to other vision tasks, e.g. re-training classifiers. |
| Starting Page | 279 |
| Ending Page | 284 |
| File Size | 773548 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.57 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Semantics Binary codes Vectors Image coding Image retrieval Principal component analysis Head matrix factorization image retrieval attribute hashing function |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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