Keyword-guided word spotting in historical printed documents using synthetic data and user feedback
International Journal on Document Analysis and Recognition
Scene completion using millions of photographs
ACM SIGGRAPH 2007 papers
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Proceedings of the 18th ACM conference on Information and knowledge management
Detecting, tracking and recognizing license plates
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Synthetically trained multi-view object class and viewpoint detection for advanced image retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Automatic license plate recognition
IEEE Transactions on Intelligent Transportation Systems
License Plate Recognition From Still Images and Video Sequences: A Survey
IEEE Transactions on Intelligent Transportation Systems
Synthesizing queries for handwritten word image retrieval
Pattern Recognition
Aggregating Local Image Descriptors into Compact Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Vehicle identification from images has been predominantly addressed through automatic license plate recognition (ALPR) techniques which detect and recognize the characters in the plate region of the image. We move away from traditional ALPR techniques and advocate for a data-driven approach for vehicle identification. Here, given a plate image region, the idea is to search for a near-duplicate image in an annotated database; if found, the identity of the near-duplicate is transferred to the input region. Although this approach could be perceived as impractical, we actually demonstrate that it is feasible with state-of-the-art image representations, and that it presents some advantages in terms of speed, and time-to-deploy. To overcome the issue of identifying previously unseen identities, we propose an image simulation approach where photo-realistic images of license plates are generated for desired plate numbers. We demonstrate that there is no perceivable performance difference between using synthetic and real plates. We also improve the matching accuracy using similarity learning, which is in the spirit of domain adaptation.