A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Semi-supervised classification with hybrid generative/discriminative methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-conditional learning: generative/discriminative training for clustering and classification
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Dual-ranking for web image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
A hybrid unsupervised image re-ranking approach with latent topic contents
Proceedings of the ACM International Conference on Image and Video Retrieval
Correlated multi-label refinement for semantic noise removal
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Machine Vision and Applications
| Hi-index | 0.00 |
The web has the potential to serve as an excellent source of example imagery for visual concepts. Image search engines based on text keywords can fetch thousands of images for a given query; however, their results tend to be visually noisy. We present a technique that allows a user to refine noisy search results and characterize a more precise visual object class. With a small amount of user intervention we are able to re-rank search engine results to obtain many more examples of the desired concept. Our approach is based on semi-supervised machine learning in a novel probabilistic graphical model composed of both generative and discriminative elements. Learning is achieved via a hybrid expectation maximization / expected gradient procedure initialized with a small example set defined by the user. We demonstrate our approach on images of musical instruments collected from Google image search. The rankings given by our model show significant improvement with respect to the user-refined query. The results are suitable for improving user experience in image search applications and for collecting large labeled datasets for computer vision research.