Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Automatically Labeling Video Data Using Multi-class Active Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
FaceTracer: A Search Engine for Large Collections of Images with Faces
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Why Is Facial Occlusion a Challenging Problem?
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Spatio-temporal tube kernel for actor retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Active Learning Methods for Interactive Image Retrieval
IEEE Transactions on Image Processing
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In this article, we propose a new video object retrieval system. Our approach is based on a Spatio-Temporal data representation, a dedicated kernel design and a statistical learning toolbox for video object recognition and retrieval. Using state-of-the-art video object detection algorithms (for faces or cars, for example) we segment video object tracks from real movies video shots. We then extract, from these tracks, sets of spatio-temporally coherent features that we call Spatio-Temporal Tubes. To compare these complex tube objects, we design a Spatio-Temporal Tube Kernel (STTK) function. Based on this kernel similarity we present both supervised and active learning strategies embedded in Support Vector Machine framework. Additionally, we propose a multi-class classification framework dealing with unbalanced data. Our approach is successfully evaluated on two real movies databases, the french movie "L'esquive" and episodes from "Buffy, the Vampire Slayer" TV series. Our method is also tested on a car database (from real movies) and shows promising results for car identification task.