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
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Scalable mining of small visual objects
Proceedings of the 20th ACM international conference on Multimedia
| Hi-index | 0.00 |
State-of-the-art visual search methods allow retrieving efficiently small rigid objects in very large image datasets (e.g. logos, paintings, etc.). User's perception of the classical query-by-window paradigm is however affected by the fact that many submitted queries actually return nothing or only junk results. We demonstrate in this demo that the perception can be radically different if the objects of interest are rather suggested to the user by pre-computing relevant clusters of instances. Impressive results involving very small objects discovered in a web collection of 110K images are demonstrated through a simple interactive GUI.