Multitraining support vector machine for image retrieval
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Image Processing, 2006, 15 (11), pp. 3597 - 3601
- Issue Date:
- 2006-11-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
 | 2011000298OK.pdf | 1.4 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Relevance feedback (RF) schemes based on support vector machines (SVMs) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based RF approaches is often poor when the number of labeled feedback samples is small. This is mainly due to 1) the SVM classifier being unstable for small-size training sets because its optimal hyper plane is too sensitive to the training examples; and 2) the kernel method being ineffective because the feature dimension is much greater than the size of the training samples. In this paper, we develop a new machine learning technique, multitraining SVM (MTSVM), which combines the merits of the cotraining technique and a random sampling method in the feature space. Based on the proposed MTSVM algorithm, the above two problems can be mitigated. Experiments are carried out on a large image set of some 20 000 images, and the preliminary results demonstrate that the developed method consistently improves the performance over conventional SVM-based RFs in terms of precision and standard deviation, which are used to evaluate the effectiveness and robustness of a RF algorithm, respectively. © 2006 IEEE.
Please use this identifier to cite or link to this item: