Pictorial Structures for Object Recognition
International Journal of Computer Vision
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Towards Multi-View Object Class Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Multi-view Object Detection Based on Spatial Consistency in a Low Dimensional Space
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Intelligent query: open another door to 3d object retrieval
Proceedings of the international conference on Multimedia
Discriminative mixture-of-templates for viewpoint classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Objects as attributes for scene classification
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Synthesizing queries for handwritten word image retrieval
Pattern Recognition
Data-driven vehicle identification by image matching
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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This paper proposes a novel approach to multi-view object class and viewpoint detection for the retrieval of images showing one or several objects from a given viewpoint, a viewpoint range or any viewpoint in image databases. All detectors are trained exclusively on a few synthetic 3D models without any manual bounding-box, viewpoint or part annotation, making object class and viewpoint detection a scalable learning task. Previous work on this topic relies on the detection of object parts for each individual viewpoint, ignoring the responses of part detectors specific to other viewpoints. Instead, we explicitly exploit appearance ambiguities caused by spurious detections of parts under more than one viewpoint by combining all detector responses in a joint spatial pyramid encoding. We achieve state-of-the-art results in multi-view object class detection and viewpoint determination on current benchmarking data sets and demonstrate increased robustness to partial occlusion.