Learning to learn
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Affine-Invariant Local Descriptors and Neighborhood Statistics for Texture Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning From a Small Number of Training Examples by Exploiting Object Categories
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 6 - Volume 06
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Efficient Image Matching with Distributions of Local Invariant Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Online multiclass learning by interclass hypothesis sharing
ICML '06 Proceedings of the 23rd international conference on Machine learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Towards Scalable Dataset Construction: An Active Learning Approach
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Shape-Based Object Localization for Descriptive Classification
International Journal of Computer Vision
A unified contour-pixel model for figure-ground segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Pattern Recognition Letters
Bottom-up perceptual organization of images into object part hypotheses
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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Current object recognition systems aim at recognizing numerous object classes under limited supervision conditions. This paper provides a benchmark for evaluating progress on this fundamental task. Several methods have recently proposed to utilize the commonalities between object classes in order to improve generalization accuracy. Such methods can be termed interclass transfer techniques. However, it is currently difficult to asses which of the proposed methods maximally utilizes the shared structure of related classes. In order to facilitate the development, as well as the assessment of methods for dealing with multiple related classes, a new dataset including images of several hundred mammal classes, is provided, together with preliminary results of its use. The images in this dataset are organized into five levels of variability, and their labels include information on the objects' identity, location and pose. From this dataset, a classification benchmark has been derived, requiring fine distinctions between 72 mammal classes. It is then demonstrated that a recognition method which is highly successful on the Caltech101, attains limited accuracy on the current benchmark (36.5%). Since this method does not utilize the shared structure between classes, the question remains as to whether interclass transfer methods can increase the accuracy to the level of human performance (90%). We suggest that a labeled benchmark of the type provided, containing a large number of related classes is crucial for the development and evaluation of classification methods which make efficient use of interclass transfer.