Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Similarity metric learning for a variable-kernel classifier
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Adjustment Learning and Relevant Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Boosting Mixture Models for Semi-supervised Learning
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
IEEE Transactions on Image Processing
Boosting the distance estimation
Pattern Recognition Letters
Online multiclass learning by interclass hypothesis sharing
ICML '06 Proceedings of the 23rd international conference on Machine learning
Kernel-based distance metric learning for content-based image retrieval
Image and Vision Computing
Handle local optimum traps in CBIR systems
Proceedings of the 2008 ACM symposium on Applied computing
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Automatic Database Creation and Object's Model Learning
Knowledge Acquisition: Approaches, Algorithms and Applications
Sided and symmetrized Bregman centroids
IEEE Transactions on Information Theory
Image annotation with tagprop on the MIRFLICKR set
Proceedings of the international conference on Multimedia information retrieval
Supervised learning of similarity measures for content-based 3D model retrieval
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
Adapting visual category models to new domains
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Distance metric learning for content identification
IEEE Transactions on Information Forensics and Security
Neighborhood preserving regression for image retrieval
Neurocomputing
A multi-scale learning framework for visual categorization
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Object flow: learning object displacement
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Learning from pairwise constraints by Similarity Neural Networks
Neural Networks
Predicting protein-peptide binding affinity by learning peptide-peptide distance functions
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
A latent variable ranking model for content-based retrieval
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Random forests for metric learning with implicit pairwise position dependence
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards the Optimal Discriminant Subspace
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Clothing-to-words mapping using word separation method
Computers and Electrical Engineering
A picture is worth a thousand tags: automatic web based image tag expansion
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Who is repinning?: predicting a brand's user interactions using social media retrieval
Proceedings of the Thirteenth International Workshop on Multimedia Data Mining
Pairwise support vector machines and their application to large scale problems
The Journal of Machine Learning Research
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Image retrieval critically relies on the distance function used to compare a query image to images in the database. We suggest to learn such distance functions by training binary classifiers with margins, where the classifiers are defined over the product space of pairs of images. The classifiers are trained to distinguish between pairs in which the images are from the same class and pairs which contain images from different classes. The signed margin is used as a distance function. We explore several variants of this idea, based on using SVM and Boosting algorithms as product space classifiers. Our main contribution is a distance learning method which combines boosting hypotheses over the product space with a weak learner based on partitioning the original feature space. The weak learner used is a Gaussian mixture model computed using a constrained EM algorithm, where the constraints are equivalence constraints on pairs of data points. This approach allows us to incorporate unlabeled data into the training process. Using some benchmark databases from the UCI repository, we show that our margin based methods significantly outperform existing metric learning methods, which are based on learning a Mahalanobis distance. We then show comparative results of image retrieval in a distributed learning paradigm, using two databases: a large database of facial images (YaleB), and a database of natural images taken from a commercial CD. In both cases our GMM based boosting method outperforms all other methods, and its generalization to unseen classes is superior.