Proceedings Article10.1109/CVPR.2006.202
Multiple Object Class Detection with a Generative Model
Krystian Mikolajczyk,Bastian Leibe,Bernt Schiele +2 more
- 17 Jun 2006
- Vol. 1, pp 26-36
TL;DR: The performance of the proposed multi-object class detection approach is competitive to state of the art approaches dedicated to a single object class recognition problem.
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Abstract: In this paper we propose an approach capable of simultaneous recognition and localization of multiple object classes using a generative model. A novel hierarchical representation allows to represent individual images as well as various objects classes in a single, scale and rotation invariant model. The recognition method is based on a codebook representation where appearance clusters built from edge based features are shared among several object classes. A probabilistic model allows for reliable detection of various objects in the same image. The approach is highly efficient due to fast clustering and matching methods capable of dealing with millions of high dimensional features. The system shows excellent performance on several object categories over a wide range of scales, in-plane rotations, background clutter, and partial occlusions. The performance of the proposed multi-object class detection approach is competitive to state of the art approaches dedicated to a single object class recognition problem.
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Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
- 20 Jun 2005
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
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Visual categorization with bags of keypoints
Gabriela Csurka
- 01 Jan 2004
TL;DR: This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches and shows that it is simple, computationally efficient and intrinsically invariant.
Feature Detection with Automatic Scale Selection
TL;DR: It is shown how the proposed methodology applies to the problems of blob detection, junction detection, edge detection, ridge detection and local frequency estimation and how it can be used as a major mechanism in algorithms for automatic scale selection, which adapt the local scales of processing to the local image structure.
The PASCAL visual object classes challenge 2006 (VOC2006) results
Mark Everingham,Andrew Zisserman,Christopher Williams,Luc Van Gool +3 more
- 01 Jan 2006
TL;DR: This report presents the results of the 2006 PASCAL Visual Object Classes Challenge (VOC2006).
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