Efficient Sample Mining for Object Detection
| Type of publication: | Conference paper |
| Citation: | Canevet_ACML_2014 |
| Publication status: | Published |
| Booktitle: | Proceedings of the 6th Asian Conference on Machine Learning (ACML) |
| Series: | JMLR: Workshop and Conference Proceedings |
| Volume: | 29 |
| Year: | 2014 |
| Month: | November |
| Location: | Nha Trang, Vietnam |
| Abstract: | We propose a novel approach to efficiently select informative samples for large-scale learning. Instead of directly feeding a learning algorithm with a very large amount of samples, as it is usually done to reach state-of-the-art performance, we have developed a “distillation” procedure to recursively reduce the size of an initial training set using a criterion that ensures the maximization of the information content of the selected sub-set. We demonstrate the performance of this procedure for two different computer vision problems. First, we show that distillation can be used to improve the traditional bootstrapping approach to object detection. Second, we apply distillation to a classification problem with artificial distortions. We show that in both cases, using the result of a distillation process instead of a random sub-set taken uniformly in the original sample set improves performance significantly. |
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| Added by: | [UNK] |
| Total mark: | 0 |
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