Name
Abstract | ||
|---|---|---|
Experiential image retrieval systems aim to provide the user with a natural and intuitive search experience. The goal is to empower the user to navigate large collections based on his own needs and preferences, while simultaneously providing him with an accurate sense of what the database has to offer. In this paper we integrate a new browsing mechanism called deep exploration with the proven technique of retrieval by relevance feedback. In our approach, relevance feedback focuses the search on relevant regions, while deep exploration facilitates transparent navigation to promising regions of feature space that would normally remain unreachable. Optimal feature weights are determined automatically based on the evidential support for the relevance of each single feature. To achieve efficient refinement of the search space, images are ranked and presented to the user based on their likelihood of being useful for further exploration. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1631272.1631385 | ACM Multimedia 2001 |
Keywords | Field | DocType |
experiential image retrieval system,intuitive search experience,accurate sense,search space,efficient refinement,relevance feedback,deep exploration,single feature,optimal feature weight,feature space,image retrieval,feature selection | Experiential learning,Computer vision,Feature vector,Relevance feedback,Information retrieval,Ranking,Computer science,Feature (computer vision),Image retrieval,Artificial intelligence,Content-based image retrieval,Visual Word | Conference |
Citations | PageRank | References |
3 | 0.36 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bart Thomee | 1 | 773 | 39.96 |
| Mark J. Huiskes | 2 | 922 | 34.00 |
| Erwin Bakker | 3 | 17 | 1.67 |
| Michael S. Lew | 4 | 2742 | 166.02 |