Unifying textual and visual cues for content-based image retrieval on the World Wide Web
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Libraries: Meeting Place for Low-Level And High-Level Vision
ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Formulating Semantic Image Annotation as a Supervised Learning Problem
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Minimum probability of error image retrieval
IEEE Transactions on Signal Processing
Semantic concept-based query expansion and re-ranking for multimedia retrieval
Proceedings of the 15th international conference on Multimedia
Exploring multimedia in a keyword space
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Image retrieval using query by contextual example
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
MS '08 Proceedings of the 2nd ACM workshop on Multimedia semantics
Semantic queries in databases: problems and challenges
Proceedings of the 18th ACM conference on Information and knowledge management
Cov-HGMEM: an improved hierarchical clustering algorithm
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
A review on automatic image annotation techniques
Pattern Recognition
Proceedings of the 16th International Conference on Extending Database Technology
Hi-index | 0.00 |
A solution to the problem of image retrieval based on query-by-semantic-example (QBSE) is presented. QBSE extends the idea of query-by-example to the domain of semantic image representations. A semantic vocabulary is first defined, and a semantic retrieval system is trained to label each image with the posterior probability of appearance of each concept in the vocabulary. The resulting vector is interpreted as the projection of the image onto a semantic probability simplex, where a suitable similarity function is defined. Queries are specified by example images, which are projected onto the probability simplex. The database images whose projections on the simplex are closer to that of the query are declared its closest neighbors. Experimental evaluation indicates that 1) QBSE significantly outperforms the traditional query-by-visual-example paradigm when the concepts in the query image are known to the retrieval system, and 2) has equivalent performance even in the worst case scenario of queries composed by unknown concepts.