Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel approach to content-based image retrieval with relevance feedback, which is based on the random walker algorithm introduced in the context of interactive image segmentation. The idea is to treat the relevant and non-relevant images labeled by the user at every feedback round as ''seed'' nodes for the random walker problem. The ranking score for each unlabeled image is computed as the probability that a random walker starting from that image will reach a relevant seed before encountering a non-relevant one. Our method is easy to implement, parameter-free and scales well to large datasets. Extensive experiments on different real datasets with several image similarity measures show the superiority of our method over different recent approaches. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.patcog.2011.03.016 | Pattern Recognition |
Keywords | Field | DocType |
relevance feedback,different recent approach,random walker algorithm,non-relevant image,unlabeled image,content-based image retrieval,interactive image segmentation,image similarity measure,random walker,random walker problem,random walks,different real datasets,random walk | Relevance feedback,Random walk,Image retrieval,Image segmentation,Artificial intelligence,Random walker algorithm,Computer vision,Automatic image annotation,Ranking,Pattern recognition,Content-based image retrieval,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
44 | 9 | Pattern Recognition |
Citations | PageRank | References |
20 | 0.73 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel Rota Bulò | 1 | 564 | 33.69 |
| Massimo Rabbi | 2 | 20 | 0.73 |
| Marcello Pelillo | 3 | 1888 | 150.33 |