Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-Shot Learning of Object Categories
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
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
Modeling LSH for performance tuning
Proceedings of the 17th ACM conference on Information and knowledge management
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Making image to class distance comparable
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Hamming embedding similarity-based image classification
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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
Randomized spatial partition for scene recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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
Locality discriminative coding for image classification
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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Naive Bayes Nearest Neighbor (NBNN) is a feature-based image classifier that achieves impressive degree of accuracy [1] by exploiting 'Image-to-Class' distances and by avoiding quantization of local image descriptors. It is based on the hypothesis that each local descriptor is drawn froma class-dependent probability measure. The density of the latter is estimated by the non-parametric kernel estimator, which is further simplified under the assumption that the normalization factor is class-independent. While leading to significant simplification, the assumption underlying the original NBNN is too restrictive and considerably degrades its generalization ability. The goal of this paper is to address this issue. As we relax the incriminated assumption we are faced with a parameter selection problem that we solve by hinge-loss minimization. We also show that our modified formulation naturally generalizes to optimal combinations of feature types. Experiments conducted on several datasets show that the gain over the original NBNN may attain up to 20 percentage points. We also take advantage of the linearity of optimal NBNN to perform classification by detection through efficient sub-window search [2], with yet another performance gain. As a result, our classifier outperforms--in terms of misclassification error--methods based on support vector machine and bags of quantized features on some datasets.