Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
On the Resemblance and Containment of Documents
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
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
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Non-negative matrix factorisation for object class discovery and image auto-annotation
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A posteriori multi-probe locality sensitive hashing
MM '08 Proceedings of the 16th ACM international conference on Multimedia
The Relevant-Set Correlation Model for Data Clustering
Statistical Analysis and Data Mining
Object Mining Using a Matching Graph on Very Large Image Collections
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Query expansion for hash-based image object retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Logo retrieval with a contrario visual query expansion
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A unified framework for object retrieval and mining
IEEE Transactions on Circuits and Systems for Video Technology
Large-Scale Discovery of Spatially Related Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Unsupervised Object Discovery: A Comparison
International Journal of Computer Vision
Co-clustering analysis of weblogs using bipartite spectral projection approach
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Multi-source shared nearest neighbours for multi-modal image clustering
Multimedia Tools and Applications
Consistent visual words mining with adaptive sampling
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
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State-of-the-art visual search systems allow to retrieve efficiently small rigid objects in very large datasets. They are usually based on the query-by-window paradigm: a user selects any image region containing an object of interest and the system returns a ranked list of images that are likely to contain other instances of the query object. User's perception of these tools is however affected by the fact that many submitted queries actually return nothing or only junk results (complex non-rigid objects, higher-level visual concepts, etc.). In this paper, we address the problem of suggesting only the object's queries that actually contain relevant matches in the dataset. This requires to first discover accurate object's clusters in the dataset (as an offline process); and then to select the most relevant objects according to user's intent (as an on-line process). We therefore introduce a new object's instances clustering framework based on a major contribution: a bipartite shared-neighbours clustering algorithm that is used to gather object's seeds discovered by matching adaptive and weighted sampling. Shared nearest neighbours methods were not studied beforehand in the case of bipartite graphs and never used in the context of object discovery. Experiments show that this new method outperforms state-of-the-art object mining and retrieval results on the Oxford Building dataset. We finally describe two object-based visual query suggestion scenarios using the proposed framework and show examples of suggested object queries.