Name
Abstract | ||
|---|---|---|
Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multilabel image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multilabel images, we proposed a multilabel SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICIP.2004.1421535 | Image Processing, 2004. ICIP '04. 2004 International Conference |
Keywords | Field | DocType |
computer vision,image classification,learning (artificial intelligence),realistic images,support vector machines,artificial data,max loss strategy,mean max loss strategy,multilabel svm active learning,real-world image,support vector machine,svm,active,classification,active learning,learning artificial intelligence,image | Computer vision,Automatic image annotation,Active learning,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Contextual image classification,Machine learning | Conference |
Volume | ISSN | ISBN |
4 | 1522-4880 | 0-7803-8554-3 |
Citations | PageRank | References |
49 | 1.84 | 4 |
Authors | ||
3 | ||