Abstract
This article presents a scene-driven (i.e. bottom-up) visual saliency detection technique for videos. The proposed method utilizes non-negative matrix factorization (NMF) to replicate neural responses of primary visual cortex neurons in spatial domain. In temporal domain, principal component analysis (PCA) was applied to imitate the effect of stimulus change experience during neural adaptation phenomena. We apply the proposed saliency model to background subtraction problem. The proposed method does not rely on any background model and is purely unsupervised. In experimental results, it will be shown that the proposed method competes well with some of the state-of-the-art background subtraction techniques especially in dynamic scenes.
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Borji, A.: Boosting bottom-up and top-down visual features for saliency estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)
Rahtu, E., Kannala, J., Blaschko, M.B.: Learning a category independent object detection cascade. In: IEEE International Conference on Computer Vision (2011)
Rutishauser, U., Walther, D., Koch, C., Perona, P.: Is bottom-up attention useful for object recognition? In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, pp. II-37–II-44 (2004)
Kanan, C., Cottrell, G.: Robust classification of objects, faces, and flowers using natural image statistics. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2472–2479 (2010)
Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Salient Region Detection and Segmentation. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 66–75. Springer, Heidelberg (2008)
Rahtu, E., Kannala, J., Salo, M., Heikkilä, J.: Segmenting salient objects from images and videos. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 366–379. Springer, Heidelberg (2010)
Frintrop, S.: General object tracking with a component-based target descriptor. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 4531–4536 (2010)
Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. Trans. Img. Proc. 19(1), 185–198 (2010)
Lu, T., Yuan, Z., Huang, Y., Wu, D., Yu, H.: Video retargeting with nonlinear spatial-temporal saliency fusion. In: Proceedings of the 2010 IEEE 17th International Conference on Image Processing (2010)
Jacobson, N., Lee, Y.L., Mahadevan, V., Vasconcelos, N., Nguyen, T.: A novel approach to fruc using discriminant saliency and frame segmentation. IEEE Transactions on Image Processing 19(11), 2924–2934 (2010)
Marchesotti, L., Cifarelli, C., Csurka, G.: A framework for visual saliency detection with applications to image thumbnailing. In: IEEE 12th International Conference on Computer Vision, pp. 2232–2239 (2009)
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: 2010 IEEE Conference Computer Vision and Pattern Recognition (CVPR), pp. 2376–2383 (2010)
Bouwmans, T.: Recent advanced statistical background modeling for foreground detection: A systematic survey. Recent Patents on Computer Science 4(3), 147–176 (2011)
Calderara, S., Melli, R., Prati, A., Cucchiara, R.: Reliable background suppression for complex scenes. In: Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks, pp. 211–214 (2006)
Heikkilä, J., Silvn, O.: A real-time system for monitoring of cyclists and pedestrians. Image and Vision Computing 22(7), 563–570 (2004)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. xxiii+637+663 (1999)
Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)
Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000)
Mahadevan, V., Vasconcelos, N.: Spatiotemporal saliency in dynamic scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(1), 171–177 (2010)
Gao, D., Mahadevan, V., Vasconcelos, N.: On the plausibility of the discriminant center-surround hypothesis for visual saliency. Journal of Vision 8(7) (2008)
Itti, L., Baldi, P.: Bayesian surprise attracts human attention. Vision Research 49(10), 1295–1306 (2009)
Olshausen, B., Field, D.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607–609 (1996)
Tsotsos, J.K., Bruce, N.D.B.: Saliency based on information maximization. In: Weiss, Y., Schölkopf, B., Platt, J. (eds.) Advances in Neural Information Processing Systems, vol. 18, pp. 155–162. MIT Press (2006)
Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: Sun: A bayesian framework for saliency using natural statistics. Journal of Vision 8(7) (2008)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Hoyer, P.O.: Modeling receptive fields with non-negative sparse coding. Neurocomputing 52–54, 547–552 (2003)
Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5, 1457–1469 (2004)
Rajapakse, M., Wyse, L.: Nmf vs ica for face recognition. In: Guo, M. (ed.) ISPA 2003. LNCS, vol. 2745, pp. 605–610. Springer, Heidelberg (2003)
Olmos, A., Kingdom, F.A.A.: A biologically inspired algorithm for the recovery of shading and reflectance images. Perception 33, 1463–1473 (2004)
Barnich, O., Van Droogenbroeck, M.: Vibe: A universal background subtraction algorithm for video sequences. IEEE Transactions on Image Processing 20(6), 1709–1724 (2011)
Maddalena, L., Petrosino, A.: The sobs algorithm: What are the limits. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 21–26 (2012)
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Tavakoli, H.R., Rahtu, E., Heikkilä, J. (2013). Saliency Detection Using Joint Temporal and Spatial Decorrelation. In: Kämäräinen, JK., Koskela, M. (eds) Image Analysis. SCIA 2013. Lecture Notes in Computer Science, vol 7944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38886-6_66
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DOI: https://doi.org/10.1007/978-3-642-38886-6_66
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