CONF
grangier:2006:icann/IDIAP
A Neural Network to Retrieve Images from Text Queries
Grangier, David
Bengio, Samy
EXTERNAL
https://publications.idiap.ch/attachments/reports/2006/grangier_icann06.pdf
PUBLIC
https://publications.idiap.ch/index.php/publications/showcite/grangier:2006:idiap-06-33
Related documents
International Conference on Artificial Neural Networks (ICANN)
2
24-34
2006
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.
REPORT
grangier:2006:idiap-06-33/IDIAP
A Neural Network to Retrieve Images from Text Queries
Grangier, David
Bengio, Samy
EXTERNAL
https://publications.idiap.ch/attachments/reports/2006/grangier_rr06-33.pdf
PUBLIC
Idiap-RR-33-2006
2006
IDIAP
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.