Photobook: content-based manipulation of image databases
International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Semantic representation: search and mining of multimedia content
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multimedia semantic indexing using model vectors
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Minimum probability of error image retrieval
IEEE Transactions on Signal Processing
Bridging the Gap: Query by Semantic Example
IEEE Transactions on Multimedia
Using manual and automated annotations to search images by semantic similarity
Multimedia Tools and Applications
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Current image retrieval techniques have difficulties to retrieve images which exhibit distinct visual patterns but belong to the class of the query image. Previous attempts to improve generalization have shown that the introduction of semantic representations can mitigate this problem. We extend the existing query-by-semantic example (QBSE) retrieval paradigm by adding a second layer of semantic representation. At the first level, the representation is driven by patch-based visual features. Semantic concepts, from a predefined vocabulary, are modeled as Gaussian mixtures on a visual feature space, and images as vectors of posterior probabilities of containing each of the semantic concepts. At the second level, the representation is purely semantic. Semantic concepts are modeled as Dirichlet mixtures on the semantic feature space of QBSE, and images are again represented as vectors of posterior concept probabilities. It is shown that the proposed retrieval strategy, referred to as query-by-contextual-example (QBCE), is able to cope with the ambiguities of patch-based classification, exhibiting significantly better generalization than previous methods. An experimental evaluation on benchmark datasets shows that QBCE retrieval systems can substantially outperform their QBVE and QBSE counterparts, achieving high precision at most levels of recall.