The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification
Abstract
We address the problem of visual category recognition by learning an image-to-image distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patch-based feature vectors common in object recognition work as a basis for our image-to-image distance functions. Our large-margin formulation for learning the distance functions is similar to formulations used in the machine learning literature on distance metric learning, however we differ in that we learn local distance functions-a different parameterized function for every image of our training set-whereas typically a single global distance function is learned. This was a novel approach first introduced in Frome, Singer, & Malik, NIPS 2006. In that work we learned the local distance functions independently, and the outputs of these functions could not be compared at test time without the use of additional heuristics or training. Here we introduce a different approach that has the advantage that it learns distance functions that are globally consistent in that they can be directly compared for purposes of retrieval and classification. The output of the learning algorithm are weights assigned to the image features, which is intuitively appealing in the computer vision setting: some features are more salient than others, and which are more salient depends on the category, or image, being considered. We train and test using the Caltech 101 object recognition benchmark.
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Cites
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Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Learning to detect natural image boundaries using local brightness, color, and texture cues
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Distance Metric Learning for Large Margin Nearest Neighbor Classification
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In defense of Nearest-Neighbor based image classification
Boiman, O., Shechtman, E., & Irani, M. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587598 · Cited by 1,072
Parikh, D., & Grauman, K. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126281 · Cited by 851
Is that you? Metric learning approaches for face identification
Guillaumin, M., Verbeek, J., & Schmid, C. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459197 · Cited by 757
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
Wang, W., Yu, R., Huang, Q., & Neumann, U. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00272 · Cited by 544
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
Faghri, F., Fleet, D. J., Kiros, J., & Fidler, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.05612 · Cited by 543
Metadata
- Authors
- Frome, A., Singer, Y., Sha, F., & Malik, J.
- Venue
- 2007 IEEE 11th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2007
- Pages
- 1-8
- OpenAlex
- W2129156852
- Cited by
- 347
- References
- 26
- FWCI
- 24.36
- Type
- conference paper
- Language
- en
- Open access
- closed
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