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
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
Discriminative Training for Object Recognition Using Image Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Stable distributions, pseudorandom generators, embeddings, and data stream computation
Journal of the ACM (JACM)
An empirical analysis of the probabilistic K-nearest neighbour classifier
Pattern Recognition Letters
A Discriminative Kernel-Based Approach to Rank Images from Text Queries
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
On supervision and statistical learning for semantic multimedia analysis
Journal of Visual Communication and Image Representation
The segmented and annotated IAPR TC-12 benchmark
Computer Vision and Image Understanding
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In this paper we introduce a new approach aimed at solving the problem of image retrieval from text queries. We propose to estimate the word relevance of an image using a neighborhood-based estimator. This estimation is obtained by counting the number of word-relevant images among the K-neighborhood of the image. To this end a Bayesian approach is adopted to define such a neighborhood. The local estimations of all the words that form a query are naively combined in order to score the images according to that query. The experiments show that the results are better and faster than the state-of-theart techniques. A special consideration is done for the computational behaviour and scalability of the proposed approach.