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Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning sparse metrics via linear programming
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Dimensionality Reduction by Learning an Invariant Mapping
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Online Passive-Aggressive Algorithms
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Information-theoretic metric learning
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A Discriminative Kernel-Based Approach to Rank Images from Text Queries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast solvers and efficient implementations for distance metric learning
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Low-Rank Kernel Learning with Bregman Matrix Divergences
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Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
Learning to retrieve images from text queries with a discriminative model
AMR'06 Proceedings of the 4th international conference on Adaptive multimedia retrieval: user, context, and feedback
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Block-Based methods for image retrieval using local binary patterns
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Mining social images with distance metric learning for automated image tagging
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An online metric learning approach through margin maximization
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
One shot similarity metric learning for action recognition
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
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Online learning in the embedded manifold of low-rank matrices
The Journal of Machine Learning Research
A latent variable ranking model for content-based retrieval
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Metric learning for large scale image classification: generalizing to new classes at near-zero cost
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Online metric learning methods using soft margins and least squares formulations
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Online multi-modal distance learning for scalable multimedia retrieval
Proceedings of the sixth ACM international conference on Web search and data mining
Adaptive regularization of weight vectors
Machine Learning
Image retrieval using eigen queries
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Exploring the similarities of neighboring spatiotemporal points for action pair matching
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Online multimodal deep similarity learning with application to image retrieval
Proceedings of the 21st ACM international conference on Multimedia
Guaranteed classification via regularized similarity learning
Neural Computation
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Learning a measure of similarity between pairs of objects is an important generic problem in machine learning. It is particularly useful in large scale applications like searching for an image that is similar to a given image or finding videos that are relevant to a given video. In these tasks, users look for objects that are not only visually similar but also semantically related to a given object. Unfortunately, the approaches that exist today for learning such semantic similarity do not scale to large data sets. This is both because typically their CPU and storage requirements grow quadratically with the sample size, and because many methods impose complex positivity constraints on the space of learned similarity functions. The current paper presents OASIS, an Online Algorithm for Scalable Image Similarity learning that learns a bilinear similarity measure over sparse representations. OASIS is an online dual approach using the passive-aggressive family of learning algorithms with a large margin criterion and an efficient hinge loss cost. Our experiments show that OASIS is both fast and accurate at a wide range of scales: for a data set with thousands of images, it achieves better results than existing state-of-the-art methods, while being an order of magnitude faster. For large, web scale, data sets, OASIS can be trained on more than two million images from 150K text queries within 3 days on a single CPU. On this large scale data set, human evaluations showed that 35% of the ten nearest neighbors of a given test image, as found by OASIS, were semantically relevant to that image. This suggests that query independent similarity could be accurately learned even for large scale data sets that could not be handled before.