Abstract
Salient detection approaches mainly use single local cues or global cues as its inputs features to detect salient objects, which are sensitive to complex background, so the effect of detection were not satisfactory. In this paper, we investigate the traits of saliency detection and observed the two following facts: Firstly, high-level saliency cues achieve better saliency detection results than low-level saliency cues. Secondly, multi-difference cues achieve better saliency detection results than single difference cues. Based on deeply analysis, we proposed an image saliency detection algorithm through high level multi-difference cues (HMDS). By using multi-difference, not only HMDS could remove the non-salient region effectively, but also it could enhance the pixel value of salient region at the same time. In order to evaluate the performance of HMDS, the proposed method is compared with seven state-of-the-art algorithms on five popular datasets. The final experimental results show that the proposed method performs effectiveness, and will have a perfect application prospect.
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Qin, Y., Lu, H., Xu, Y., et al:. Saliency detection via cellular automata. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 110–119. IEEE Computer Society (2015)
Frintrop, S., Werner, T., García, G.M.: Traditional saliency reloaded: a good old model in new shape. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 82–90 (2015)
Mauthner, T., Possegger, H., Waltner, G., et al.: Encoding based saliency detection for videos and images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2494–2502 (2015)
Wan, S., Jin, P., Yue, L.: An approach for image retrieval based on visual saliency. In: International Conference on Image Analysis and Signal Processing, IASP 2009, pp. 172–175. IEEE (2009)
Chen, T., Cheng, M.M., Tan, P., et al.: Sketch2Photo: internet image montage. ACM Trans. Graph. (TOG) 28(5), 124 (2009)
Vig, E., Dorr, M., Cox, D.: Space-variant descriptor sampling for action recognition based on saliency and eye movements. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7578, pp. 84–97. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33786-4_7
Rapantzikos, K., Avrithis, Y., Kollias, S.: Dense saliency-based spatiotemporal feature points for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1454–1461. IEEE (2009)
Cheng, M., Mitra, N.J., Huang, X., et al.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 11, 1254–1259 (1998)
Borji, A., Itti, L.: State-of-the-art in visual attention modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 185–207 (2013)
Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of 14th Annual ACM International Conference on Multimedia, pp. 815–824. ACM (2006)
Jiang, H., Wang, J., Yuan, Z., et al.: Automatic salient object segmentation based on context and shape prior. In: BMVC, vol. 6, no. 7, p. 9 (2011)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)
Achanta, R., Shaji, A., Smith, K., et al:. SLIC superpixels (2010)
Borji, A., Sihite, D.N., Itti, L.: Salient object detection: a benchmark. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, pp. 414–429. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33709-3_30
Achanta, R., Hemami, S., Estrada, F., et al.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1597–1604. IEEE (2009)
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012)
Margolin, R., Tal, A., Zelnik-Manor, L.: What makes a patch distinct? In: IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), pp. 1139–1146. IEEE (2013)
Liu, T., Yuan, Z., Sun, J., et al.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)
Alpert, S., Galun, M., Brandt, A., et al.: Image segmentation by probabilistic bottom-up aggregation and cue integration. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 315–327 (2012)
Movahedi, V., Elder, J.: Design and perceptual validation of performance measures for salient object segmentation. In: CVPRW, vol. 5, pp. 49–56 (2010)
Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)
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This work is supported by the General Scientific Research Program of Universities in Dalian City Scientific and Technological Projects (2015A11GX022).
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Sun, J., Wu, J., Yu, H., Zhang, M., Luo, Q., Sun, J. (2017). High-Level Multi-difference Cues for Image Saliency Detection. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_43
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DOI: https://doi.org/10.1007/978-981-10-6385-5_43
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