Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
Synthetically trained multi-view object class and viewpoint detection for advanced image retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
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This paper describes a new approach for detecting objects based on measuring the spatial consistency between different parts of an object. These parts are pre-defined on a set of training images and then located in any arbitrary image. Each part is represented by a group of densely sampled SIFT features. Supervised Locally Linear Embedding is then used to describe the appearance of each part in a low dimensional space. The novelty of this approach is that linear embedding techniques are used to model each object part and the background in the same coordinate space. This permits the detection algorithm to explicitly label test features as belonging to an object part or background. A spatial consistency algorithm is then employed to find object parts that together provide evidence for the location of object(s) in the image. Experiments on the 3D and PASCAL VOC datasets yield results comparable and often superior to those found in the literature.