Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Aardvark to Zorro: A Benchmark for Mammal Image Classification
International Journal of Computer Vision
Learning with Few Examples by Transferring Feature Relevance
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Semantic hierarchies for image annotation: A survey
Pattern Recognition
What makes a good detector? --- structured priors for learning from few examples
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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In the last few years, object detection techniques have progressed immensely. Impressive detection results have been achieved for many objects such as faces [11, 14, 9] and cars [11]. The robustness of these systems emerges from a training stage utilizing thousands of positive examples. One approach to enable learning from a small set of training examples is to find an efficient set of features that accurately represent the target object. Unfortunately, automatically selecting such a feature set is a difficult task in itself. In this paper we present a novel feature selection method that is based on the notion of object categories. We assume that when learning to recognize a new object (like an apple) we also know a category it belongs to (fruit). We further assume that features that are useful for learning other objects in the same category (e.g. pear or orange) will also be useful for learning the novel object. This leads to a simple criterion for selecting features and building classifiers. We show that our method gives significant improvement in detection performance in challenging domains.