Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Image-to-class distance metric learning for image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Semantic label sharing for learning with many categories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A scalable dual approach to semidefinite metric learning
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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Learning a proper distance metric is crucial for many computer vision and image classification applications. Neighborhood Components Analysis (NCA) is an effective distance metric learning method which maximizes the kNN leave-out-one score on the training data by considering visual similarity between images. However, only using visual similarity to learn image distances could not satisfactorily cope with the diversity and complexity of a large number of real images with many concepts. To overcome this problem, integrating concrete semantic relations of images into the distance metric learning procedure can be a useful solution. This can more accurately model the image similarities and better reflect the perception of human in the classification system. In this paper, we propose Semantic NCA (SNCA), a novel approach which integrates semantic similarity into NCA, where neighborhood relations between images in the training dataset are measured by both visual characteristics and their concept relations. We evaluated several semantic similarity measures based on the WordNet tree. Experimental results show that the proposed approach improves the performance compared to the traditional distance metric learning methods.