Abstract
A complete and discriminative dictionary can achieve superior performance. However, it also consumes extra processing time and memory, especially for large datasets. Most existing compact dictionary learning methods need to set the dictionary size manually, therefore an appropriate dictionary size is usually obtained in an exhaustive search manner. How to automatically learn a compact dictionary with high fidelity is still an open challenge. We propose an automatic compact dictionary learning (ACDL) method which can guarantee a more compact and discriminative dictionary while at the same time maintaining the state-of-the-art classification performance. We incorporate two innovative components in the formulation of the dictionary learning algorithm. First, an indicator function is introduced that automatically removes highly correlated dictionary atoms with weak discrimination capacity. Second, two additional constraints, namely, the sum-to-one and the non-negative constraints are imposed on the sparse coefficients. On one hand, this achieves the same functionality as the \(L_2\)-normalization on the raw data to maintain a stable sparsity threshold. On the other hand, this effectively preserves the geometric structure of the raw data which would be otherwise destroyed by the \(L_2\)-normalization. Extensive evaluations have shown that the preservation of geometric structure of the raw data plays an important role in achieving high classification performance with smallest dictionary size. Experimental results conducted on four recognition problems demonstrate the proposed ACDL can achieve competitive classification performance using a drastically reduced dictionary (https://github.com/susanqq/ACDL.git).
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References
Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: a strategy employed by V1? Vis. Res. 37, 3311–3325 (1997)
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15, 3736–3745 (2006)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54, 4311 (2006)
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 689–696. ACM (2009)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)
Ramirez, I., Sprechmann, P., Sapiro, G.: Classification and clustering via dictionary learning with structured incoherence and shared features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3501–3508. IEEE (2010)
Zhang, Q., Li, B.: Discriminative K-SVD for dictionary learning in face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2691–2698. IEEE (2010)
Jiang, Z., Lin, Z., Davis, L.S.: Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2651–2664 (2013)
Yang, M., Zhang, L., Feng, X., Zhang, D.: Fisher discrimination dictionary learning for sparse representation. In: IEEE International Conference on Computer Vision (ICCV), pp. 543–550. IEEE (2011)
Mairal, J., Ponce, J., Sapiro, G., Zisserman, A., Bach, F.R.: Supervised dictionary learning. In: Advances in Neural Information Processing Systems, pp. 1033–1040 (2009)
Mairal, J., Bach, F., Ponce, J.: Task-driven dictionary learning. IEEE Trans. Pattern Anal. Mach. Intell. 34, 791–804 (2012)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)
Rahimpour, A., Taalimi, A., Luo, J., Qi, H.: Distributed object recognition in smart camera networks. In: IEEE International Conference on Image Processing, Phoenix, Arizona, USA. IEEE (2016)
Lazebnik, S., Raginsky, M.: Supervised learning of quantizer codebooks by information loss minimization. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1294–1309 (2009)
Liu, J., Shah, M.: Learning human actions via information maximization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)
Kong, S., Wang, D.: A dictionary learning approach for classification: separating the particularity and the commonality. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 186–199. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33718-5_14
Jiang, Z., Zhang, G., Davis, L.S.: Submodular dictionary learning for sparse coding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3418–3425. IEEE (2012)
Yang, M., Dai, D., Shen, L., Van Gool, L.: Latent dictionary learning for sparse representation based classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4138–4145. IEEE (2014)
Qiu, Q., Patel, V.M., Chellappa, R.: Information-theoretic dictionary learning for image classification. IEEE Trans. Pattern Anal. Mach. Intell. 36, 2173–2184 (2014)
Lu, C., Shi, J., Jia, J.: Scale adaptive dictionary learning. IEEE Trans. Image Process. 23, 837–847 (2014)
Parikh, N., Boyd, S.: Proximal algorithms. Found. Trends Optim. 1, 123–231 (2013)
Rakotomamonjy, A.: Direct optimization of the dictionary learning problem. IEEE Trans. Sig. Process. 61, 5495–5506 (2013)
Naikal, N., Yang, A.Y., Sastry, S.S.: Towards an efficient distributed object recognition system in wireless smart camera networks. In: 13th Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2010)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 2169–2178. IEEE (2006)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: CVPR Workshop on Generative-Mode Based Vision. IEEE (2004)
Naikal, N., Yang, A.Y., Sastry, S.S.: Informative feature selection for object recognition via sparse PCA. In: IEEE International Conference on Computer Vision (ICCV), pp. 818–825. IEEE (2011)
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: DECAF: a deep convolutional activation feature for generic visual recognition. In: Proceedings of the 31st International Conference on Machine Learning (ICML-2014), pp. 647–655 (2014)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10590-1_53
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems, pp. 487–495 (2014)
Gao, S., Tsang, I.W.H., Chia, L.T.: Laplacian sparse coding, hypergraph laplacian sparse coding, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35, 92–104 (2013)
Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y.: What is the best multi-stage architecture for object recognition? In: IEEE International Conference on Computer Vision (ICCV), pp. 2146–2153. IEEE (2009)
Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)
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Song, Y., Zhang, Z., Liu, L., Rahimpour, A., Qi, H. (2017). Dictionary Reduction: Automatic Compact Dictionary Learning for Classification. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10111. Springer, Cham. https://doi.org/10.1007/978-3-319-54181-5_20
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DOI: https://doi.org/10.1007/978-3-319-54181-5_20
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