Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
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
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
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast solvers and efficient implementations for distance metric learning
Proceedings of the 25th international conference on Machine learning
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Learning instance-to-class distance for human action recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Discriminative compact pyramids for object and scene recognition
Pattern Recognition
Making image to class distance comparable
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Positive semidefinite metric learning using boosting-like algorithms
The Journal of Machine Learning Research
Dog breed classification using part localization
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Learning class-to-image distance via large margin and l1-norm regularization
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Improving image distance metric learning by embedding semantic relations
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Learning image-to-class distance metric for image classification
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Naive bayes image classification: beyond nearest neighbors
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Object class detection: A survey
ACM Computing Surveys (CSUR)
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Image-To-Class (I2C) distance is first used in Naive-Bayes Nearest-Neighbor (NBNN) classifier for image classification and has successfully handled datasets with large intra-class variances. However, the performance of this distance relies heavily on the large number of local features in the training set and test image, which need heavy computation cost for nearest-neighbor (NN) search in the testing phase. If using small number of local features for accelerating the NN search, the performance will be poor. In this paper, we propose a large margin framework to improve the discrimination of I2C distance especially for small number of local features by learning Per-Class Mahalanobis metrics. Our I2C distance is adaptive to different class by combining with the learned metric for each class. These multiple Per-Class metrics are learned simultaneously by forming a convex optimization problem with the constraints that the I2C distance from each training image to its belonging class should be less than the distance to other classes by a large margin. A gradient descent method is applied to efficiently solve this optimization problem. For efficiency and performance improved, we also adopt the idea of spatial pyramid restriction and learning I2C distance function to improve this I2C distance. We show in experiments that the proposed method can significantly outperform the original NBNN in several prevalent image datasets, and our best results can achieve state-of-the-art performance on most datasets.