Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
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
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
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
Computer Vision and Image Understanding
Universal and Adapted Vocabularies for Generic Visual Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localizing Objects with Smart Dictionaries
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval
Computer Vision and Image Understanding
Supervised Learning of Quantizer Codebooks by Information Loss Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning non-redundant codebooks for classifying complex objects
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Online semi-supervised discriminative dictionary learning for sparse representation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Multiview Hessian discriminative sparse coding for image annotation
Computer Vision and Image Understanding
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Visual dictionary learning and base (binary) classifier training are two basic problems for the recently most popular image categorization framework, which is based on the bag-of-visual-terms (BOV) models and multiclass SVM classifiers. In this paper, we study new algorithms to improve performance of this framework from these two aspects. Typically SVM classifiers are trained with dictionaries fixed, and as a result the traditional loss function can only be minimized with respect to hyperplane parameters (w and b). We propose a novel loss function for a binary classifier, which links the hinge-loss term with dictionary learning. By doing so, we can further optimize the loss function with respect to the dictionary parameters. Thus, this framework is able to further increase margins of binary classifiers, and consequently decrease the error bound of the aggregated classifier. On two benchmark dataset, Graz [1] and the fifteen scene category dataset [2], our experiment results significantly outperformed state-of-the-art works.