Robust Real-Time Face Detection
International Journal of Computer Vision
Papier-Mache: toolkit support for tangible input
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition from a single image per person: A survey
Pattern Recognition
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Eyepatch: prototyping camera-based interaction through examples
Proceedings of the 20th annual ACM symposium on User interface software and technology
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Improving classifiers with unlabeled weakly-related videos
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Circuits and Systems for Video Technology
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Recently, object recognition has been successfully implemented in a couple of multimedia content annotation and retrieval applications. The employed recognition approaches are carefully selected and adapted to the specific needs of their tasks. In this work, we propose a framework to automate the simultaneous selection and customization of the entire recognition process. This framework only requires an annotated set of sample images or videos and precisely specified task requirements to select an appropriate setup among thousands of possibilities. We use an efficient recognition infrastructure and iterative analysis strategies to make this approach practicable for real-world applications. A case study for face recognition from a single image per person demonstrates the capabilities of this holistic approach.