Abstract
Recently, many saliency detection models use image boundary as an effective prior of image background for saliency extraction. However, these models may fail when the salient object is overlapped with the boundary. In this paper, we propose a novel saliency detection model by computing the contrast between superpixels with background priors and introducing a refinement method to address the problem in existing studies. Firstly, the SLIC (Simple Linear Iterative Clustering) method is used to segment the input image into superpixels. Then, the feature difference is calculated between superpixels based on the color histogram. The initial saliency value of each superpixel is computed as the sum of feature differences between this superpixel and other ones in image boundary. Finally, a saliency map refinement method is used to reassign the saliency value of each image pixel to obtain the final saliency map for images. Compared with other state-of-the-art saliency detection methods, the proposed saliency detection method can provide better saliency prediction results for images by the measure from precision, recall and F-measure on two widely used datasets.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, L., Xie, X., Ma, W., Zhang, H., Zhou, H.: Image adaptation based on attention model for small-form-factor devices. In: ICME (2003)
Ouerhani, N., Bracamonte, J., Hugli, H., Ansorge, M., Pellandini, F.: Adaptive color image compression based on visual attention. In: ICIAP (2001)
Stentiford, F.: A visual attention estimator applied to image subject enhancement and colour and grey Level Compression. In: ICPR (2004)
Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. In: CVPR (2007)
Achanta, R., Hemami, S.S., Estrada, F.J., Ssstrunk, S.: Frequency tuned salient region detection. In: CVPR (2009)
Cheng, M., Zhang, G., Mitra, N., Huang, X., Hu, S.: Global contrast based salient region detection. In: CVPR (2011)
Fang, Y., Chen, Z., Lin, W., Lin, C.: Saliency detection in the compressed domain for adaptive image retargeting. IEEE Trans. Image Process. 21(9), 3888–3901 (2012)
Wei, Y., Wen, F., Zhu, W., Sun, J.: Geodesic saliency using background priors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 29–42. Springer, Heidelberg (2012)
Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. In: CVPR (2012)
Xie, Y., Lu, H., Yang, M.: Bayesian saliency via low and mid level cues. IEEE Trans. Image Process. 22(5), 1689–1698 (2013)
Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.: Saliency detection via graph-based manifold ranking. In: CVPR (2013)
Tseng, P., Carmi, R., Cameron, I., Munoz, D., Itti, L.: Quantifying center bias of observers in free viewing of dynamic natural scenes. J. Vis. 9(7:4), 1–16 (2009)
Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)
Achanta, R., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels. Technical report, EPFL. Technical report: 149300(3) (2010)
Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: CVPR (2013)
Grauman, K., Darrell, T.: Fast contour matching using approximate earth movers distance. In: CVPR (2004)
Liu, Z., Zou, W., Meur, O.: Saliency tree: a novel saliency detection framework. IEEE Trans. Image Process. 23(5), 1937–1952 (2014)
Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: CVPR (2014)
Jiang, H., Wang, J., Yuan, Z., Liu, T., Zheng, N., Li, S.: Automatic salient object segmentation based on contex and shape prior. In: BMVC (2011)
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)
Tian, H., Fang, Y., Zhao, Y., Lin, W., Ni, R., Zhu, Z.: Salient region detection by fusing bottom-up and top-down features extracted from a single image. IEEE Trans. Image Process. 23(10), 4389–4398 (2014)
Acknowledgements
This research was supported by Singapore MOE Tier 1 funding (RG 36/11: M4010981), and the Rapid-Rich Object Search (ROSE) Lab at the Nanyang Technological University, Singapore. The ROSE Lab is supported by the National Research Foundation, Prime Ministers Office, Singapore, under its IDM Futures Funding Initiative and administered by the Interactive and Digital Media Programme Office.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xi, T., Fang, Y., Lin, W., Zhang, Y. (2015). Improved Salient Object Detection Based on Background Priors. 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 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-24075-6_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24074-9
Online ISBN: 978-3-319-24075-6
eBook Packages: Computer ScienceComputer Science (R0)