Abstract
Salient objects detection aims to locate objects that capture human attention within images. Recent progresses in saliency detection have exploited the center prior, to combine with other cues such as background information, object size or region contrast, achieving competitive results. However, previous approaches of center prior supposing salient object locates nearly at image center is very simple, fragile, especially not suitable for multiple salient objects detection, but the assumption is mostly heuristic. In this paper, we present an adaptive location method based on geodesic filtering framework to address these issues. First, we detect salient points by the adjustive color Harris algorithm. Second, we involve the Affinity Propagation (AP) method to automatically cluster the salient points for a coarse objects location. Then, we utilize geodesic filtering framework for a final saliency map by multiplying objects location and size. Experimental results on two more challenging databases of off-center and multiple salient objects demonstrate our approach is more robust to the location variations of salient objects, against state-of-the-art methods for saliency detection.
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
References
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)
Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. ArXiv e-prints (2015)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 19(1), 185–198 (2010)
Li, Q., Zhou, Y., Yang, J.: Saliency based image segmentation. In: 2011 International Conference on Multimedia Technology (ICMT), pp. 5068–5071. IEEE (2011)
Li, Y., Fu, K., Zhou, L., Qiao, Y., Yang, J.: Saliency detection via foreground rendering and background exclusion. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 3263–3267. IEEE (2014)
Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 280–287. IEEE (2014)
Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 733–740. IEEE (2012)
Ren, Z., Gao, S., Chia, L.T., Tsang, I.H.: Region-based saliency detection and its application in object recognition. IEEE Trans. Circ. Syst. Video Technol. 24(5), 769–779 (2014)
Stalder, S., Grabner, H., Van Gool, L.: Dynamic objectness for adaptive tracking. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part III. LNCS, vol. 7726, pp. 43–56. Springer, Heidelberg (2013)
Van De Weijer, J., Gevers, T., Bagdanov, A.D.: Boosting color saliency in image feature detection. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 150–156 (2006)
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)
Xie, Y., Lu, H.: Visual saliency detection based on bayesian model. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 645–648. IEEE (2011)
Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via l 0 gradient minimization. ACM Trans. Graph. (TOG) 30, 174 (2011). ACM
Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3166–3173. IEEE (2013)
Zhao, L., Liang, S., Wei, Y., Jia, J.: Size and location matter: a new baseline for salient object detection. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9005, pp. 578–592. Springer, Heidelberg (2015)
Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2814–2821. IEEE (2014)
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
Jia, S. et al. (2015). Adaptive Location for Multiple Salient Objects Detection. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_46
Download citation
DOI: https://doi.org/10.1007/978-3-319-26555-1_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26554-4
Online ISBN: 978-3-319-26555-1
eBook Packages: Computer ScienceComputer Science (R0)