Beyond Spatial Pyramids: Receptive Field Learning for Pooled Image Features
Title | Beyond Spatial Pyramids: Receptive Field Learning for Pooled Image Features |
Publication Type | Conference Paper |
Year of Publication | 2012 |
Authors | Jia, Y., Huang C., & Darrell T. |
Page(s) | 3370-3377 |
Other Numbers | 3284 |
Abstract | In this paper we examine the effect of receptive field designson classification accuracy in the commonly adoptedpipeline of image classification. While existing algorithmsusually use manually defined spatial regions for pooling, weshow that learning more adaptive receptive fields increasesperformance even with a significantly smaller codebook sizeat the coding layer. To learn the optimal pooling parameters,we adopt the idea of over-completeness by startingwith a large number of receptive field candidates, and traina classifier with structured sparsity to only use a sparse subsetof all the features. An efficient algorithm based on incrementalfeature selection and retraining is proposed for fastlearning. With this method, we achieve the best publishedperformance on the CIFAR-10 dataset, using a much lowerdimensional feature space than previous methods. |
URL | http://www.icsi.berkeley.edu/pubs/vision/beyondspatial12.pdf |
Bibliographic Notes | Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), Providence, Rhode Island, pp. 3370-3377 |
Abbreviated Authors | Y. Jia, C. Huang, and T. Darrell |
ICSI Research Group | Vision |
ICSI Publication Type | Article in conference proceedings |