Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Efficient object category recognition using classemes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Energy minimization under constraints on label counts
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Local Distance Functions: A Taxonomy, New Algorithms, and an Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual and semantic similarity in ImageNet
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Scalable object-class retrieval with approximate and top-k ranking
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A graph-matching kernel for object categorization
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
| Hi-index | 0.00 |
In this work we introduce novel image metrics that can be used with distance-based classifiers or directly to decide whether two input images belong to the same class. While most prior image distances rely purely on comparisons of low-level features extracted from the inputs, our metrics use a large database of labeled photos as auxiliary data to draw semantic relationships between the two images, beyond those computable from simple visual features. In a preprocessing stage our approach derives a semantic image graph from the labeled dataset, where the nodes are the labeled images and the edges connect pictures with related labels. The graph can be viewed as modeling a semantic image manifold, and it enables the use of graph distances to approximate semantic distances. Thus, we reformulate the task of measuring the semantic distance between two unlabeled pictures as the problem of embedding the two input images in the semantic graph. We propose and evaluate several embedding schemes and graph distance metrics. Our results on Caltech101, Caltech256 and ImageNet show that our distances consistently match or outperform the state-of-the-art in this field.