Machine Learning
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Machine Learning
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Fast Keypoint Recognition Using Random Ferns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal medical image retrieval: image categorization to improve search precision
Proceedings of the international conference on Multimedia information retrieval
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Overview of the second workshop on medical content---based retrieval for clinical decision support
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Introduced as a new subtask of the ImageCLEF 2010 challenge, we aim at recognizing the modality of a medical image based on its content only. Therefore, we propose to rely on a representation of images in terms of words from a visual dictionary. To this end, we introduce a very fast approach that allows the learning of implicit dictionaries which permits the construction of compact and discriminative bag of visual words. Instead of a unique computationally expensive clustering to create the dictionary, we propose a multiple random partitioning method based on Extreme Random Subspace Projection Ferns. By concatenating these multiple partitions, we can very efficiently create an implicit global quantization of the feature space and build a dictionary of visual words. Taking advantages of extreme randomization, our approach achieves very good speed performance on a real medical database, and this for a better accuracy than K-means clustering.