The Strength of Weak Learnability
Machine Learning
Polynomial-Time Geometric Matching for Object Recognition
International Journal of Computer Vision
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Machine Learning
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Using Dependent Regions for Object Categorization in a Generative Framework
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
The Journal of Machine Learning Research
Computer description of curved objects
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
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We propose a new supervised object retrieval method based on the selection of local visual features learned with the BLasso algorithm. BLasso is a boosting-like procedure that efficiently approximates the Lasso path through backward regularization steps. The advantage compared to a classical boosting strategy is that it produces a sparser selection of visual features. This allows us to improve the efficiency of the retrieval and, as discussed in the paper, it facilitates human visual interpretation of the models generated. We carried out our experiments on the Caltech-256 dataset with state-of-the-art local visual features. We show that our method outperforms AdaBoost in effectiveness while significantly reducing the model complexity and the prediction time. We discuss the evaluation of the visual models obtained in terms of human interpretability.