Labeled Images Verification Using Gaussian Mixture Models
| Type of publication: | Inproceedings |
| Citation: | betj09:acmsac |
| Booktitle: | 24th Annual ACM Symposium on Applied Computing (ACM SAC 09), Honolulu, USA, March 8 - 12 |
| Year: | 2009 |
| Pages: | 1331--1336 |
| URL: | http://www.hennebert.org/downl... |
| DOI: | 10.1145/1529282.1529581 |
| Abstract: | We are proposing in this paper an automated system to verify that images are correctly associated to labels. The novelty of the system is in the use of Gaussian Mixture Models (GMMs) as statistical modeling scheme as well as in several improvements introduced specifically for the verification task. Our approach is evaluated using the Caltech 101 database. Starting from an initial baseline system providing an equal error rate of 27.4%, we show that the rate of errors can be reduced down to 13% by introducing several optimizations of the system. The advantage of the approach lies in the fact that basically any object can be generically and blindly modeled with limited supervision. A potential target application could be a post-filtering of images returned by search engines to prune out or reorder less relevant images. |
| Keywords: | Content-based image retrieval, GMM, machine learning, Pattern Recognition |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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