Name
Abstract | ||
|---|---|---|
In region-based image retrieval, not all the regions are important for retrieving similar images and rather, the user is often interested in performing a query on only salient regions. Therefore, we propose a new method for extraction of salient regions using support vector machines (SVM) and a method for importance score learning according to the user's interaction. Once an image is segmented, our algorithm permits the attention window (AW) according to the variation of an image and selects salient regions by using the pre-defined feature vector and SVM within the AW. By using SVM, we do not need to determine the heuristic feature parameters and produce more reasonable results. The distance values from SVM are used for initial importance scores of salient regions and our proposed updating algorithm using relevance feedback updates them automatically. Through performance comparison with parametric salient extraction method, our proposed method shows better performance as well as semantic query interface for object-level image retrieval. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICPR.2004.1334422 | ICPR (2) |
Keywords | Field | DocType |
extraction method,svm-based salient region,heuristic feature parameter,similar image,learning (artificial intelligence),image segmentation,salient region,parametric salient extraction method,salient region extraction method,svm,object-level image retrieval,importance score learning,feature extraction,better performance,image retrieval,importance score,relevance feedback,semantic query interface,region-based image retrieval,content-based retrieval,support vector machines,new method,attention window,feature vector,support vector machine,learning artificial intelligence | Computer vision,Feature vector,Relevance feedback,Pattern recognition,Computer science,Support vector machine,Image retrieval,Image segmentation,Feature extraction,Semantic query,Artificial intelligence,Salient | Conference |
Volume | ISSN | ISBN |
2 | 1051-4651 | 0-7695-2128-2 |
Citations | PageRank | References |
9 | 0.63 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| ByoungChul Ko | 1 | 241 | 23.28 |
| Sooyeong Kwak | 2 | 39 | 5.65 |
| Hyeran Byun | 3 | 505 | 65.97 |