The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Ask the locals: Multi-way local pooling for image recognition
Abstract
Invariant representations in object recognition systems are generally obtained by pooling feature vectors over spatially local neighborhoods. But pooling is not local in the feature vector space, so that widely dissimilar features may be pooled together if they are in nearby locations. Recent approaches rely on sophisticated encoding methods and more specialized codebooks (or dictionaries), e.g., learned on subsets of descriptors which are close in feature space, to circumvent this problem. In this work, we argue that a common trait found in much recent work in image recognition or retrieval is that it leverages locality in feature space on top of purely spatial locality. We propose to apply this idea in its simplest form to an object recognition system based on the spatial pyramid framework, to increase the performance of small dictionaries with very little added engineering. State-of-the-art results on several object recognition benchmarks show the promise of this approach.
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Metadata
- Authors
- Boureau, Y.-L., Le Roux, N., Bach, F., Ponce, J., & LeCun, Y.
- Venue
- 2011 International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 2651-2658
- OpenAlex
- W2160692033
- Cited by
- 277
- References
- 12
- FWCI
- 24.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Boureau, Y.-L., Le Roux, N., Bach, F., Ponce, J., & LeCun, Y. (2011). Ask the locals: Multi-way local pooling for image recognition. 2011 International Conference on Computer Vision. https://doi.org/10.1109/iccv.2011.6126555
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