The nature of statistical learning theory
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Creating Efficient Codebooks for Visual Recognition
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
International Journal of Computer Vision
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Video diver: generic video indexing with diverse features
Proceedings of the international workshop on Workshop on multimedia information retrieval
A comparison of color features for visual concept classification
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Performance evaluation of local colour invariants
Computer Vision and Image Understanding
Foundations and Trends in Information Retrieval
Real-time bag of words, approximately
Proceedings of the ACM International Conference on Image and Video Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the CLEF 2009 large-scale visual concept detection and annotation task
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Performance measures for multilabel evaluation: a case study in the area of image classification
Proceedings of the international conference on Multimedia information retrieval
New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative
Proceedings of the international conference on Multimedia information retrieval
Proceedings of the international conference on Multimedia information retrieval
Minimum explanation complexity for MOD based visual concept detection
Proceedings of the international conference on Multimedia information retrieval
Overview of the CLEF 2009 large-scale visual concept detection and annotation task
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
A fast visual word frequency - inverse image frequency for detector of rare concepts
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Overview of the photo annotation task in imageCLEF@ICPR
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
The university of surrey visual concept detection system at imageCLEF@ICPR: working notes
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Graph-based methods for the automatic annotation and retrieval of art prints
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Automatic annotation of tagged content using predefined semantic concepts
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Artistic image classification: an analysis on the PRINTART database
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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Our group within the University of Amsterdam participated in the large-scale visual concept detection task of ImageCLEF 2009. Our experiments focus on increasing the robustness of the individual concept detectors based on the bag-of-words approach, and less on the hierarchical nature of the concept set used. To increase the robustness of individual concept detectors, our experiments emphasize in particular the role of visual sampling, the value of color invariant features, the influence of codebook construction, and the effectiveness of kernel-based learning parameters. The participation in ImageCLEF 2009 has been successful, resulting in the top ranking for the large-scale visual concept detection task in terms of both EER and AUC. For 40 out of 53 individual concepts, we obtain the best performance of all submissions to this task. For the hierarchical evaluation, which considers the whole hierarchy of concepts instead of single detectors, using the concept likelihoods estimated by our detectors directly works better than scaling these likelihoods based on the class priors.