Abstract
Feature selection plays a crucial role in deciding the salient regions of an image as in any other pattern recognition problem. However the problem of identifying the relevant features that plays a fundamental role in saliency of an image has not received much attention so far. We introduce an unsupervised feature selection method to improve the accuracy of salient object detection. The noisy irrelevant features in the image are identified by maximizing the mixing rate of a Markov process running on a linear combination of various graphs, each representing a feature. The global optimum of this convex problem is achieved by maximizing the second smallest eigen value of the graph Laplacian via semi-definite programming. The enhanced image graph model, after the removal of irrelevant features, is shown to improve the salient object detection performance on a large image data base with annotated ‘ground truth’.
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References
Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Trans. Graph. 26(3) (2007) ISSN: 0301–0730
Boyd, S.: Convex Optimization of Graph Laplacian Eigenvalues. In: Proceedings International Congress of Mathematicians, vol. 3, pp. 1311–1319 (2006)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
Bruce, N.D., Tsotsos, J.K.: Saliency Based on Information Maximization. In: NIPS, pp. 155–162 (2005)
Chen, L.Q., Xie, X., Fan, X., Ma, W.Y., Zhang, H.J., Zhou, H.Q.: A visual attention model for adapting images on small displays. Multimedia Syst. 9, 353–364 (2003)
Chung, F.R.K.: Spectral Graph Theory. American Mathematical Society, Providence (1997)
Gao, D., Vasconcelos, N.: Discriminant Saliency for Visual Recognition from Cluttered Scenes. In: NIPS, pp. 481–488 (2004)
Gopalakrishnan, V., Hu, Y., Rajan, D.: Random walks on graphs to model saliency in images. In: CVPR, pp. 1698–1705 (2009)
Gopalakrishnan, V., Hu, Y., Rajan, D.: Salient Region Detection by Modeling Distributions of Color and Orientation. IEEE Transactions on Multimedia 11(5), 892–905 (2009)
Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming (web page and software) (2009), http://stanford.edu/~boyd/cvx
Guo, C., Ma, Q., Zhang, L.: Spatio-temporal Saliency Detection Using Phase Spectrum of Quaternion Fourier Transform. In: CVPR (2008)
Harel, J., Koch, C., Perona, P.: Graph-Based Visual Saliency. In: NIPS, pp. 545–552 (2006)
He, X., Cai, D., Niyogi, P.: Laplacian Score for Feature Selection. In: NIPS (2002)
Hou, X., Zhang, L.: Saliency Detection: A Spectral Residual Approach. In: CVPR (2007)
Itti, L., Koch, C., Niebur, E.: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)
Law, M.H.C., Figueiredo, M.A.T., Jain, A.K.: Simultaneous Feature Selection and Clustering Using Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. (2004)
Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to Detect A Salient Object. In: CVPR (2007)
Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised Feature Selection Using Feature Similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 301–312 (2002)
Norris, J.: Markov Chains. Cambridge University Press, Cambridge (1997)
Santella, A., Agrawala, M., DeCarlo, D., Salesin, D., Cohen, M.F.: Gaze-based interaction for semi-automatic photo cropping. In: CHI, pp. 771–780 (2006)
Stentiford, F.: Attention based Auto Image Cropping. In: The 5th International Conference on Computer Vision Systems (2007)
Sun, J., Boyd, S., Xiao, L., Diaconis, P.: The Fastest Mixing Markov Process on a Graph and a Connection to a Maximum Variance Unfolding Problem. SIAM Review 48, 681–699 (2004)
Valenti, R., Sebe, N., Gevers, T.: Image Saliency by Isocentric Curvedness and Color. In: ICCV (2009)
Volker, R., Tilman, L.: Feature selection in clustering problems. In: NIPS (2004)
Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Network 19, 1395–1407 (2006)
Zhao, B., Kwok, J., Wang, F., Zhang, C.: Unsupervised Maximum Margin Feature Selection with Manifold Regularization. In: CVPR (June 2009)
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Gopalakrishnan, V., Hu, Y., Rajan, D. (2011). Unsupervised Feature Selection for Salient Object Detection. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_2
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DOI: https://doi.org/10.1007/978-3-642-19309-5_2
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