The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Multiclass Object Recognition with Sparse, Localized Features
Abstract
We apply a biologically inspired model of visual object recognition to the multiclass object categorization problem. Our model modifies that of Serre, Wolf, and Poggio. As in that work, we first apply Gabor filters at all positions and scales; feature complexity and position/scale invariance are then built up by alternating template matching and max pooling operations. We refine the approach in several biologically plausible ways, using simple versions of sparsification and lateral inhibition. We demonstrate the value of retaining some position and scale information above the intermediate feature level. Using feature selection we arrive at a model that performs better with fewer features. Our final model is tested on the Caltech 101 object categories and the UIUC car localization task, in both cases achieving state-of-the-art performance. The results strengthen the case for using this class of model in computer vision.
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Cites
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Cited by
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412
Convolutional networks and applications in vision
LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010) · Proceedings of 2010 IEEE International Symposium on Circuits and Systems · doi:10.1109/iscas.2010.5537907 · Cited by 2,213
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153
Robust Object Recognition with Cortex-Like Mechanisms
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., & Poggio, T. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.56 · Cited by 1,667
CNP: An FPGA-based processor for Convolutional Networks
Farabet, C., Poulet, C., Han, J. Y., & LeCun, Y. (2009) · 2009 International Conference on Field Programmable Logic and Applications · doi:10.1109/fpl.2009.5272559 · Cited by 367
NeuFlow: A runtime reconfigurable dataflow processor for vision
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50 Years of object recognition: Directions forward
Andreopoulos, A., & Tsotsos, J. K. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.04.005 · Cited by 347
A dynamically configurable coprocessor for convolutional neural networks
Chakradhar, S., Sankaradas, M., Jakkula, V., & Cadambi, S. (2010) · Proceedings of the 37th annual international symposium on Computer architecture · doi:10.1145/1815961.1815993 · Cited by 343
Facial Expression Recognition Using Facial Movement Features
Zhang, L., & Tjondronegoro, D. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.13 · Cited by 253
Unsupervised learning of invariant representations
Anselmi, F., Leibo, J. Z., Rosasco, L., Mutch, J., Tacchetti, A., & Poggio, T. (2016) · Theoretical Computer Science · doi:10.1016/j.tcs.2015.06.048 · Cited by 88
Self-citation
Metadata
- Authors
- Mutch, J., & Lowe, D. G.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 1
- Pages
- 11-18
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2105464770
- Cited by
- 453
- References
- 8
- FWCI
- 45.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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