Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust Object Detection via Soft Cascade
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Extracting Subimages of an Unknown Category from a Set of Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Automatic object detection on aerial images using local descriptors and image synthesis
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Training object detectors aims at choosing specific visual attributes which are efficient and optimal for each learned object. This paper presents a new process which achieves this goal by putting all families of descriptors one wants to consider in a pool of descriptors and by letting the algorithm build a cascade with the most efficient descriptors by introducing management of very large features pools. On the one hand, the selection of a specific family of descriptors for a given application implies a deep experience of the operator on the algorithm behaviour. On the other hand, physical constraints such as computer time and memory requirements prevent us from using all available descriptors. We present here a solution which considers several families of descriptors as a pool of descriptors and builds a cascade with the most efficient descriptors. The idea developed here consists in beginning to build a cascade with one type of descriptors and then introducing new kinds of descriptors when the current descriptor family does not bring enough differentiating information anymore. In this scope, four families of descriptors are studied here: Histogram Distance on Haar Region (HDHR), Edge Orientation Histograms (EOH), Histogram Orientation Gradient (HOG) and Gabor filters. Evaluation on public data sets shows the importance of complementary features, since performances of state of the art methods are improved.