Name
Abstract | ||
|---|---|---|
In the detection of human from image using statistical learning methods, the labor cost of collecting training samples and the time cost for retraining to match the target scene are major issues. One method to reduce the work involved in sample collection is transfer learning based on boosting. However, if there is a large change between the auxiliary scene and target scene, it is difficult to apply the transfer learning. We therefore propose a hybrid transfer learning method in which two feature spaces are prepared, one of feature obtained by transfer and another of full feature search that is the same as retraining. The feature space is selectively switched on the basis of the defined training efficiency. The proposed method improving accuracy up to 8.35% compared to conventional transfer learning while accelerating training time by 3.2 times faster compared to retraining. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ACPR.2013.8 | ACPR |
Keywords | Field | DocType |
hybrid transfer,object detection,auxiliary scene,training efficiency,efficient learning,statistical learning method,hybrid transfer learning,feature space,target scene,conventional transfer,transfer learning,full feature search,learning artificial intelligence,statistical analysis,feature extraction | Semi-supervised learning,Instance-based learning,Multi-task learning,Stability (learning theory),Active learning (machine learning),Pattern recognition,Computer science,Transfer of learning,Unsupervised learning,Artificial intelligence,Feature learning,Machine learning | Conference |
ISSN | Citations | PageRank |
0730-6512 | 1 | 0.36 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masamitsu Tsuchiya | 1 | 1 | 0.36 |
| Yuji Yamauchi | 2 | 43 | 10.45 |
| fujiyoshi | 3 | 730 | 101.43 |
| Takayoshi Yamashita | 4 | 377 | 46.83 |