Name
Abstract | ||
|---|---|---|
Training a classifier for object category recognition using images on the Internet is an attractive approach due to its scalability. However a big challenge in this approach is that it is difficult to automatically obtain sets of negative samples that are guaranteed to be free of positive samples. In this paper we propose to address this challenge with a Support Vector Data Description (SVDD) classifier An SVDD classifier does not need negative images in training. It computes a hypersphere around the potentially good images in the feature space and uses this boundary to distinguish images of target visual category from outliers. Evaluation on standard test sets shows that we are able to achieve competitive classification performance using the contaminated training images from the Internet without the need for large datasets of negative examples. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761715 | 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 |
Keywords | Field | DocType |
internet,visualization,kernel,feature space,image classification,object recognition,search engines,support vector machines | Structured support vector machine,Feature vector,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Relevance vector machine,Linear classifier,Classifier (linguistics),Margin classifier,Contextual image classification,Machine learning | Conference |
ISSN | Citations | PageRank |
1051-4651 | 3 | 0.45 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaodong Yu | 1 | 56 | 3.05 |
| Daniel Dementhon | 2 | 1327 | 139.94 |
| David Doermann | 3 | 4313 | 312.70 |