Name
Abstract | ||
|---|---|---|
When support vector machine (SVM) classifier is applied to image semantic annotation, it usually encounters the problem of excessive training samples. In this paper, we propose a novel method, which is by combining learning vector quantization (LVQ) technique and SVM classifier, to improve annotation accuracy and speed. Affinity propagation algorithm-based LVQ technique is used to optimize the training set, and a few number of optimized representative feature vectors are used to train SVM. This approach not only meets the small sample size characteristic of SVM, but also greatly accelerates the training and annotating process. Comparative experimental studies confirm the validity of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s00521-011-0651-1 | Neural Computing and Applications |
Keywords | DocType | Volume |
support vector machine,Combining LVQ,image semantic annotationlearning vector quantizationsupport vector machineaffinity propagation algorithm,LVQ technique,image semantic annotation,proposed method,SVM classifier,training set,novel method,excessive training sample,optimized representative feature vector,annotation accuracy,SVM technique | Journal | 21 |
Issue | ISSN | Citations |
4 | 1433-3058 | 4 |
PageRank | References | Authors |
0.39 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Guo | 1 | 601 | 85.05 |
| Ziheng Jiang | 2 | 67 | 7.19 |
| Song Lin | 3 | 92 | 11.24 |
| Yao Yao | 4 | 9 | 2.19 |