A Neural Network to Retrieve Images from Text Queries
| Type of publication: | Conference paper |
| Citation: | grangier:2006:icann |
| Booktitle: | International Conference on Artificial Neural Networks (ICANN) |
| Volume: | 2 |
| Year: | 2006 |
| Crossref: | grangier:2006:idiap-06-33: |
| Abstract: | This work presents a neural network for the retrieval of images from text queries. The proposed network is composed of two main modules: the first one extracts a global picture representation from local block descriptors while the second one aims at solving the retrieval problem from the extracted representation. Both modules are trained jointly to minimize a loss related to the retrieval performance. This approach is shown to be advantageous when compared to previous models relying on unsupervised feature extraction: average precision over Corel queries reaches 26.2\% for our model, which should be compared to 21.6\% for PAMIR, the best alternative. |
| Userfields: | ipdmembership={learning}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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