Abstract
Efficient and robust saliency detection is a fundamental problem in computer vision field for its wide applications, such as image segmentation and image retargeting, etc. In this paper, with the aim of uniformly highlighting the salient objects and suppressing the saliency of the background in images, we propose an efficient three-stage saliency detection method. First, boundary prior and connectivity prior are used to generate coarse saliency maps. To suppress the saliency value of the cluttered background, two supergraphs together with the adjacent graph are created so that the saliency of the background regions with similar appearances which are separated by other regions can be reduced effectively. Second, a local context-based saliency propagation is proposed to refine the saliency such that regions with similar features hold similar saliency. Finally, a logistic regressor is learned to combine the three refined saliency maps into the final saliency map automatically. The proposed method improves saliency detection on many cluttered images. The experimental results on two widely used public datasets with pixel accurate salient region annotations show that our method outperforms the state-of-the-art methods.
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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)
Sun, J., Lin, H.B.: Scale and object aware image retargeting for thumbnail browsing. In: IEEE International Conference on Computer Vision, pp. 1511–1518 (2011)
Setlur, V., Lechner, T., Nienhaus, M., Gooch, B.: Retargeting images and video for preserving information saliency. IEEE Comput. Graph. Appl. 27(5), 80–88 (2007)
Fang, Y.M., Chen, Z.Z., Lin, W.S., Lin, C.W.: Saliency detection in the compressed domain for adaptive image retargeting. IEEE Trans. Image Process. 21(9), 3888–3901 (2012)
Guo, C.L., Zhang, L.M.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 19(1), 185–198 (2010)
Itti, L.: Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans. Image Process. 13(10), 1304–1318 (2004)
Rutishauser, U., Walther, D., Koch, C., Perona, P.: Is bottom-up attention useful for object recognition? In: IEEE Conference on Computer Vision and Pattern Recognition, 32, II-37-II-44 (2004)
Ren, Z.X., Gao, S.H., Chia, L.T., Tsang, T.: Region-based saliency detection and its application in object recognition. IEEE Trans. Circuits Syst. Video Technol. 24(5), 769–779 (2014)
Sharma, G., Jurie, F., Schmid, C.: Discriminative spatial saliency for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3506–3513 (2012)
Ma, Z.G., Qing, L.Y., Miao, J., Chen, X.L.: Advertisement evaluation using visual saliency based on foveated image. In: IEEE International Conference on Multimedia and Expo, pp. 914–917 (2009)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)
Chen, M.M., Zhang, G.X., Mitra, N.J., Huang, X.L., Hu, S.M.: Global contrast based salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 409–416 (2011)
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2376–2383 (2010)
Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 733–740 (2012)
Chen, M.M., Warrell, J., Lin, W.Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: IEEE International Conference on Computer Vision, pp. 1529–1536 (2013)
Fu, H.Z., Cao, X.C., Tu, Z.W.: Cluster-based co-saliency detection. IEEE Trans. Image Process. 22(10), 3766–3778 (2013)
Scharfenberger, C., Wong, A., Fergani, K., Zelek, J.S., Clausi, D.A.: Statistical textural distinctiveness for salient region detection in natural images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 979–986 (2013)
Wei, Y.C., Wen, F., Zhu, W.J., Sun, J.: Geodesic saliency using background priors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) Computer Vision ECCV 201. Lecture notes in computer science, pp. 29–42. Springer, Berlin (2012)
Zhang, J.M., Sclaroff, S.: Saliency detection: a boolean map approach. In: IEEE International Conference on Computer Vision, pp. 153–160 (2013)
Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in neural information systems (2007)
Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: The proceedings of the eleventh ACM international conference on Multimedia (2003)
Liu, T., Yuan, Z.Y., Sun, J., Wang, J.D., Zheng, N.N., Tamg, X.O., Shum, H.Y.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)
Klein, D.A., Frintrop, S.: Center-surround divergence of feature statistics for salient object detection. In: IEEE International Conference on Computer Vision, pp. 2214–2219 (2011)
Shi, K.Y., Wang, K.Z., Lu, J.B., Lin, L.: PISA: pixelwise image saliency by aggregating complementary appearance contrast measures with spatial priors. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2115–2122 (2013)
Borji, A., Itti, L.: Exploiting local and global patch rarities for saliency detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 478–485 (2012)
Lu, Y., Zhang, W., Lu, H., Xue, X.Y.: Salient object detection using concavity context. In: IEEE International Conference on Computer Vision, pp. 233–240 (2011)
Feng, J., Wei, Y.C., Tao, L.T., Zhang, C., Sun, J.: Salient object detection by composition. In: IEEE International Conference on Computer Vision, pp. 1028–1035 (2011)
Dollar, P., Zitnick, C.L.: Structured forests for fast edge detection. In: IEEE International Conference on Computer Vision, pp. 1841–1848 (2013)
Cheng, M.M., Niloy, J.M., Huang, X.L., Philip, H.S.T., Hu, S.M.: Salient object detection and segmentation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2014)
Jiang, H.Z., Wang, J.D., Yuan, Z.J., Wu, Y., Zhang, N.N., Li, S.P.: Salient object detection: a discriminative regional feature integration approach. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2083–2090 (2013)
Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: The Proceedings of the 14th annual ACM international conference on Multimedia (2006)
Hou, X.D., Zhang, L.Q.: Saliency detection: a spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Acknowledgments
We would like to thank the anonymous reviewers for their comments. This research was supported in part by the National Natural Science Foundation of China under Grant No. 61173122, No. 61262032 and No. 61440055 and the Fundamental Research Funds for the Central Universities of Central South University under Grant No. 2013zzts046.
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Communicated by T. Mei.
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Zou, B., Liu, Q., Chen, Z. et al. Saliency detection using boundary information. Multimedia Systems 22, 245–253 (2016). https://doi.org/10.1007/s00530-014-0449-y
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DOI: https://doi.org/10.1007/s00530-014-0449-y