Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computational model for visual selection
Neural Computation
Saliency, Scale and Image Description
International Journal of Computer Vision
Explanation-Based Neural Network Learning: A Lifelong Learning Approach
Explanation-Based Neural Network Learning: A Lifelong Learning Approach
Adjustment Learning and Relevant Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Face Identification across Different Poses and Illuminations with a 3D Morphable Model
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Object Recognition with Informative Features and Linear Classification
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Part-Based Statistical Models for Object Classification and Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
The CSU face identification evaluation system: its purpose, features, and structure
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Object fingerprints for content analysis with applications to street landmark localization
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Dynamic Selection of Characteristics for Feature Based Image Sequence Stabilization
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
DAVID: discriminant analysis for verification of monuments in image data
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
New Riemannian techniques for directional and tensorial image data
Pattern Recognition
Person re-identification based on global color context
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Face Recognition from Caption-Based Supervision
International Journal of Computer Vision
Efficient human action detection using a transferable distance function
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Learning implicit transfer for person re-identification
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Object class detection: A survey
ACM Computing Surveys (CSUR)
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Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object's exact identity (e.g. Bob's BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive "training" examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient "same" versus "different" classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.