Sparselet models for efficient multiclass object detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Latent hough transform for object detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Beyond bounding-boxes: learning object shape by model-driven grouping
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
An introduction to random forests for multi-class object detection
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Can our TV robustly understand human gestures?: real-time gesture localization in range data
Proceedings of the 9th European Conference on Visual Media Production
Arbitrary-Shape object localization using adaptive image grids
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Data decomposition and spatial mixture modeling for part based model
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Branch&Rank for Efficient Object Detection
International Journal of Computer Vision
Discriminative Hough context model for object detection
The Visual Computer: International Journal of Computer Graphics
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Scalability of object detectors with respect to the number of classes is a very important issue for applications where many object classes need to be detected. While combining single-class detectors yields a linear complexity for testing, multi-class detectors that localize all objects at once come often at the cost of a reduced detection accuracy. In this work, we present a scalable multi-class detection algorithm which scales sublinearly with the number of classes without compromising accuracy. To this end, a shared discriminative codebook of feature appearances is jointly trained for all classes and detection is also performed for all classes jointly. Based on the learned sharing distributions of features among classes, we build a taxonomy of object classes. The taxonomy is then exploited to further reduce the cost of multi-class object detection. Our method has linear training and sublinear detection complexity in the number of classes. We have evaluated our method on the challenging PASCAL VOC'06 and PASCAL VOC'07 datasets and show that scaling the system does not lead to a loss in accuracy.