Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Discrete Applied Mathematics - The 2001 international workshop on combinatorial image analysis (IWCIA 2001)
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a restricted Bayesian network for object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Image classification using cluster cooccurrence matrices of local relational features
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Image annotation: which approach for realistic databases?
Proceedings of the 6th ACM international conference on Image and video retrieval
Visual word pairs for automatic image annotation
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
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Object-class recognition is one of the most challenging fields of pattern recognition and computer vision. Currently, most authors represent an object as a collection of parts and their mutual spatial relations. Therefore, two types of information are extracted: local information describing each part, and contextual information describing the (spatial) context of the part, i.e. the spatial relations between the rest of the parts and the current one. We define a generalized correlogram descriptor and represent the object as a constellation of such generalized correlograms. Using this representation, both local and contextual information are gathered into the same feature space. We take advantage of this representation in the learning stage, by using a feature selection with boosting that learns both types of information simultaneously and very efficiently. Simultaneously learning both types of information proves to be a faster approach than dealing with them separately. Our method is compared with state-of-the-art object-class recognition systems by evaluating both the accuracy and the cost of the methods.