Abstract
Many real-world problems usually deal with high-dimensional data, such as images, videos, text, web documents and so on. In fact, the classification algorithms used to process these high-dimensional data often suffer from the low accuracy and high computational complexity. Therefore, we propose a framework of transforming images from a high-dimensional image space to a low-dimensional target image space, based on learning an orthogonal smooth subspace for the SIFT sparse codes (SC-OSS). It is a two stage framework for subspace learning. Firstly, a sparse coding followed by spatial pyramid max pooling is used to get the image representation. Then, the image descriptor is mapped into an orthonormal and smooth subspace to classify images in low dimension. The proposed algorithm adds the orthogonality and a Laplacian smoothing penalty to constrain the projective function coefficient to be orthogonal and spatially smooth. The experimental results on the public datasets have shown that the proposed algorithm outperforms other subspace methods.
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References
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Bai, E.: Big data: The curse of dimensionality in modeling. In: Control Conference, pp.6–13. IEEE press, Chinese (2014)
Lou, X., Huang, D., Fan, L., et al.: An image classification algorithm based on bag of visual words and multi-kernel learning. J. Multimedia 9(2), 269–277 (2014)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. 2, 2169–2178 (2006)
Yang, J., Yu, K., Gong, Y., et al.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1794–1801 (2009)
Yan, Y., Zhang, Y.: Discriminant projection embedding for face and palmprint recognition. Neurocomputing 17, 3534–3543 (2008)
Zhou, J., Jin, Z., Yang, J.: Multiscale saliency detection using principle component analysis. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE press, Brisbane (2012)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Inter science, Hoboken, NJ (2000)
Hou, C., Nie, F., Zhang, C., et al.: Learning an orthogonal and smooth subspace for image classification. Signal Process. Lett. 16(4), 303–306 (2009). IEEE press
Lee, H., Battle, A., Raina, R., et al.: Efficient sparse coding algorithms. In: Advances in neural information processing systems, pp.801–808 (2006)
Liu, F., Liu, X.: Locality enhanced spectral embedding and spatially smooth spectral regression for face recognition. In: Information and Automation, pp.299–303. IEEE press, Shenyang (2012)
Ye, J.: Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J. Mach. Learn. Res. 6, 483–502 (2005)
Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. Comput. Vis. Pattern Recogn. 2, 994–1000 (2005)
Wang, J., Yang, J., Yu, K., et al.: Locality-constrained linear coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp.3360–3367 (2010)
Acknowledgement
This paper was supported in part by National Natural Science Foundation of China (61210006, 61100141), Program for Changjiang Scholars and Innovative Research Team in University (IRT201206), the Fundamental Research Funds for the Central Universities of China (2013JBM021).
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Dai, F., Zhao, Y., Chang, D., Lin, C. (2015). Orthogonal and Smooth Subspace Based on Sparse Coding for Image Classification. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_5
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DOI: https://doi.org/10.1007/978-3-319-24078-7_5
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