logo Idiap Research Institute        
 [BibTeX] [Marc21]
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.
Projects Idiap
Authors Canévet, Olivier
Fleuret, Francois
Added by: [UNK]
Total mark: 0
  • Canevet_ACML_2014.pdf