Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Modern Information Retrieval
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A practical SVM-based algorithm for ordinal regression in image retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
Large-scale content-based audio retrieval from text queries
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Face Recognition from Caption-Based Supervision
International Journal of Computer Vision
Multimodal indexing based on semantic cohesion for image retrieval
Information Retrieval
Efficient image annotation for automatic sentence generation
Proceedings of the 20th ACM international conference on Multimedia
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This work proposes a new approach to the retrieval of images from text queries. Contrasting with previous work, this method relies on a discriminative model: the parameters are selected in order to minimize a loss related to the ranking performance of the model, i.e. its ability to rank the relevant pictures above the non-relevant ones when given a text query. In order to minimize this loss, we introduce an adaptation of the recently proposed Passive-Aggressive algorithm. The generalization performance of this approach is then compared with alternative models over the Corel dataset. These experiments show that our method outperforms the current state-of-the-art approaches, e.g. the average precision over Corel test data is 21.6% for our model versus 16.7% for the best alternative, Probabilistic Latent Semantic Analysis.