Abstract
Content-aware applications in computational photography define the relative importance of objects or actions present in an image using a saliency map. Most saliency detection algorithms learn from the human visual system and try to find relatively important content as a salient region(s). This paper attempts to improve the saliency map defined by these algorithms using an iterative process. The saliency map of an image generated by an existing saliency detection algorithm is modified by filtering the image after segmenting into foreground and background. In order to enhance the saliency map values present in the salient region, the background is filtered using an edge-aware guided filter and the foreground is enhanced using a local Laplacian filter. The number of iterations required varies according to the image content. We show that the proposed framework enhances the saliency maps generated using the state-of-the-art saliency detection algorithms both qualitatively and quantitatively.
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References
Qin, C., Zhang, G., Zhou, Y., Tao, W., Cao, Z.: Integration of the saliency-based seed extraction and random walks for image segmentation. Neurocomputing 129, 378–391 (2014)
Peng, H., Li, B., Ling, H., Hu, W., Xiong, W., Maybank, S.J.: Salient object detection via structured matrix decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 818–832 (2017)
Ouerhani, N., Bracamonte, J., Hugli, H., Ansorge, M., Pellandini, F.: Adaptive color image compression based on visual attention. In: Proceedings of 11th International Conference on Image Analysis and Processing, pp. 416–421. IEEE (2001)
Zhao, J., Chen, Y., Feng, H., Xu, Z., Li, Q.: Infrared image enhancement through saliency feature analysis based on multi-scale decomposition. Infrared Phys. Technol. 62, 86–93 (2014)
Itti, L., Koch, C., Niebur, E., et al.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, pp. 545–552 (2006)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1597–1604. IEEE (2009)
Rezazadegan Tavakoli, H., Rahtu, E., Heikkilä, J.: Fast and efficient saliency detection using sparse sampling and kernel density estimation. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 666–675. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21227-7_62
Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.-Y.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012)
Cheng, M.-M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.-M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)
Murray, N., Vanrell, M., Otazu, X., Parraga, C.A.: Saliency estimation using a non-parametric low-level vision model. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 433–440. IEEE (2011)
Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 280–287 (2014)
Fang, S., Li, J., Tian, Y., Huang, T., Chen, X.: Learning discriminative subspaces on random contrasts for image saliency analysis. IEEE Trans. Neural Netw. Learn. Syst. 28, 1095–1108 (2016)
Borji, A., Itti, L.: Exploiting local and global patch rarities for saliency detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 478–485. IEEE (2012)
Margolin, R., Tal, A., Zelnik-Manor, L.: What makes a patch distinct? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1139–1146 (2013)
Duan, L., Wu, C., Miao, J., Qing, L., Fu, Y.: Visual saliency detection by spatially weighted dissimilarity. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 473–480. IEEE (2011)
Lei, J., Wang, B., Fang, Y., Lin, W., Le Callet, P., Ling, N., Hou, C.: A universal framework for salient object detection. IEEE Trans. Multimed. 18(9), 1783–1795 (2016)
Haralick, R.M., Shanmugam, K., et al.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)
Tang, M., Ben Ayed, I., Marin, D., Boykov, Y.: Secrets of GrabCut and kernel k-means. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1555–1563 (2015)
Paris, S., Hasinoff, S.W., Kautz, J.: Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM Trans. Graph. 30(4), 68 (2011)
Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. arXiv preprint arXiv:1708.00786 (2017)
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Patel, D., Raman, S. (2018). Saliency Map Improvement Using Edge-Aware Filtering. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_19
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DOI: https://doi.org/10.1007/978-981-13-0020-2_19
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