Abstract
Accurate localization of the salient object from an image is a difficult problem when the saliency map is noisy and incomplete. A fast approach to detect salient objects from images is proposed in this paper. To well balance the size of the object and the saliency it contains, the salient object detection is first formulated with the maximum saliency density on the saliency map. To obtain the global optimal solution, a branch-and-bound search algorithm is developed to speed up the detection process. Without any prior knowledge provided, the proposed method can effectively and efficiently detect salient objects from images. Extensive results on different types of saliency maps with a public dataset of five thousand images show the advantages of our approach as compared to some state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gao, D., Vasoncelos, N.: Descriminant saliency for visual recognition from cluttered scenes. In: Advances in Neural Information Processing Systems, pp. 481–488 (2004)
Liu, F., Gleicher, M.: Video retargeting: Automating pan and scan. In: Proc. ACM Multimedia, pp. 241–250 (2006)
Bradley, A.P., Stentiford, F.W.: Visual attention for region of interest coding in jpeg 2000. Journal of Visual Communication and Image Representation 14, 232–250 (2003)
Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Liu, F., Gleicher, M.: Automatic image retargeting with fisheye-view warping. In: Proc. of ACM Symposium on User Interface Software and Technology, pp. 153–162 (2005)
Suh, B., Ling, H., Bederson, B.B., Jacobs, D.W.: Automatic thumbnail cropping and its effectiveness. In: Proc. of ACM Symposium on User Interface Software and Technology, pp. 95–104 (2003)
Hou, X., Zhang, L.: Dynamic visual attention: Searching for coding length increments. In: Neural Information Processing Systems, pp. 681–688 (2008)
Valenti, R., Sebe, N., Gevers, T.: Image saliency by isocentric curvedness and color. In: IEEE Intl. Conf. on Computer Vision (2009)
Bruce, N., Tsotsos, J.: Saliency based on information maximization. In: Neural Information Processing Systems, pp. 155–162 (2005)
Wang, Z., Li, B.: A two-stage approach to saliency detection in images. In: IEEE Intl. Conf. on Acoustics, Speech and Signal Processing, pp. 965–968 (2008)
Miyazato, K., Kimura, A., Takagi, S., Yamato, J.: Real-time estimation of human visual attention with dynamic bayesian network and mcmc-based particle filter. In: IEEE Intl. Conf. on Multimedia and Expo., pp. 250–257 (2009)
Achanta, R., Hemami, S., Estraday, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)
Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Viswanath Gopalakrishnan, Y.H., Rajan, D.: Salient region detection by modeling distributions of color and orientation. IEEE Trans. on Multimedia 11, 892–905 (2009)
Han, J., Ngan, K.N., Li, M., Zhang, H.J.: Unsupervised extraction of visual attention objects in color images. IEEE Trans. on Circuits and Systems for Video Technology 16, 141–145 (2006)
Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: Proc. ACM Multimedia, pp. 374–381 (2003)
Ko, B.C., Nam, J.Y.: Object-of-interest image segmentation based on human attention and semantic region clustering. Virtual Journal for Biomedical Optics 1, 2462–2470 (2006)
Lampert, C.H., Blaschko, M.B., Hofmann, T.: Efficient subwindow search: A branch and bound framework for object localization. IEEE Trans. on Pattern Analysis and Machine Intelligence 31, 2129–2142 (2009)
An, S., Peursum, P., Liu, W., Venkatesh, S.: Efficient algorithms for subwindow search in object detection and localization, pp. 264–271 (2009)
Lawler, E.L., Wood, D.E.: Branch-and-bound methods: A survey. Operations Research 14, 699–719 (1966)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. on System, Man and Cybernetics 9, 62–66 (1979)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Luo, Y., Yuan, J., Xue, P., Tian, Q. (2011). Saliency Density Maximization for Object Detection and Localization. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-19318-7_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19317-0
Online ISBN: 978-3-642-19318-7
eBook Packages: Computer ScienceComputer Science (R0)