Abstract
Human attention tends to get focused on the most prominent objects in a scene which are different from the background. These are termed as salient objects. The human brain perceives an object of salient type based on its difference with the surroundings in terms of color and texture. There have been many color based approaches in the past for salient object detection. In this paper, we augment information set features with color features and detect the final single salient object using a set of color, size and location based features. The information set features result from representing the uncertainty in the color and illumination components. To locate the salient parts of the image, we make use of the entropy to find the uncertainties in the color and luminance components of the image. Extensive comparisons with the state-of-the-art methods in terms of precision, recall and F-Measure are made on two different publicly available datasets to prove the effectiveness of this approach.
Keywords
- Membership Function
- Salient Object
- Conditional Random Field
- Saliency Detection
- Connected Component Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of the Eleventh ACM International Conference on Multimedia, New York, NY, USA, pp. 374–381. ACM (2003)
Chen, L.Q., Xie, X., Fan, X., Ma, W.Y., Zhang, H.J., Zhou, H.Q.: A visual attention model for adapting images on small displays. Multimedia Syst. 9, 353–364 (2003)
Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1915–1926 (2012)
Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H.S., Hu, S.M.: Global contrast based salient region detection. IEEE TPAMI 37, 569–582 (2015)
Dhar, S., Ordonez, V., Berg, T.: High level describable attributes for predicting aesthetics and interestingness. In: 2011 IEEE Conference on CVPR, pp. 1657–1664 (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, 1254–1259 (1998)
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, 353–367 (2011)
Achanta, R., Estrada, F.J., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 66–75. Springer, Heidelberg (2008)
Achanta, R., Susstrunk, S.: Saliency detection using maximum symmetric surround. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 2653–2656 (2010)
Bruce, N.D., Tsotsos, J.K.: Saliency, attention, and visual search: An information theoretic approach. J. Vis. 9, 1–24 (2009)
Margolin, R., Tal, A., Zelnik-Manor, L.: What makes a patch distinct? In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1139–1146 (2013)
Mamta, H.M.: Robust ear based authentication using local principal independent components. Expert Syst. Appl. 40, 6478–6490 (2013)
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)
Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: A benchmark. ArXiv e-prints (2015)
Chang, K.Y., Liu, T.L., Chen, H.T., Lai, S.H.: Fusing generic objectness and visual saliency for salient object detection. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 914–921 (2011)
Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: Sun: A bayesian framework for saliency using natural statistics. J. Vis. 8(7), 32 (2008)
Achanta, R., Hemami, S., Estrada, F., Ssstrunk, S.: Frequency-tuned salient region detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR ), pp. 1597–1604 (2009)
Duan, L., Wu, C., Miao, J., Qing, L., Fu, Y.: Visual saliency detection by spatially weighted dissimilarity. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 473–480 (2011)
Cheng, M.M., Warrell, J., Lin, W.Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: IEEE ICCV, pp. 1529–1536 (2013)
Erdem, E., Erdem, A.: Visual saliency estimation by nonlinearly integrating features using region covariances. J. Vis. 13, 1–20 (2013)
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)
Jiang, H., Wang, J., Yuan, Z., Liu, T., Zheng, N., Li, S.: Automatic salient object segmentation based on context and shape prior. In: BMVC. vol. 6 (2011)
Harel, J., Koch, C., Perona, P.: Graph based visual saliency. In: NIPS, pp. 545–552 (2007)
Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: A discriminative regional feature integration approach. In: 2013 IEEE Conference on (CVPR), pp. 2083–2090. IEEE (2013)
Yang, C., Zhang, L., Lu, H.: Graph-regularized saliency detection with convex-hull-based center prior. Sign. Process. Lett. IEEE 20, 637–640 (2013)
Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 815–824. ACM (2006)
Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: CVPR 2007, pp. 1–8. IEEE (2007)
Fu, K., Gong, C., Yang, J., Zhou, Y.: Salient object detection via color contrast and color distribution. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 111–122. Springer, Heidelberg (2013)
Zhou, L., Fu, K., Li, Y., Qiao, Y., He, X., Yang, J.: Bayesian salient object detection based on saliency driven clustering. Sign. Process. Image Commun. 29, 434–447 (2014)
Aytekin, C., Kiranyaz, S., Gabbouj, M.: Automatic object segmentation by quantum cuts. In: Pattern Recognition (ICPR), pp. 112–117. IEEE (2014)
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Kapoor, A., Biswas, K.K., Hanmandlu, M. (2015). Effective Information and Contrast Based Saliency Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_18
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DOI: https://doi.org/10.1007/978-3-319-27863-6_18
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