Abstract
Saliency computation has wide applications. It is now being used in almost all vision-related applications. But, identifying saliency is still a problem. Various computational models have been proposed for identifying saliency. Global contrast-based method is used extensively. This method computes the contrast by measuring the color difference between image and specified region. It produces saliency with non-salient points, but, in the process, it loses some structural and spatial information. To address these limitations, the proposed method, i.e., Poisson-based probabilistic contrast, produces saliency with the concave topographical surface. This surface encloses the prominent object with all its structural and spatial information, or with all the salient features. Then, it is used as a reference plane for regional depth, color and spatial saliency integration. The proposed method has three stages. In the first stage, a probabilistic contrast is computed using Poisson-based maximum likelihood estimation by addition of chrominance and luminance contrast. The luminance contrast is normalized by proposed “enhance and suppress luminance method.” In the second stage, the regional color, depth, and spatial saliencies are integrated into the topographical surface to enhance the saliency. In the third and last stages, i.e., saliency enhancement stage, central saliency is used on global color distinction. The proposed method is evaluated on the publicly available datasets. Their performance is compared with 12 state-of-the-art methods. The experimental result presented here shows that the proposed method performs better.
Similar content being viewed by others
References
Durand, T., Mordan, T., Thome, N., Cord, M.: Wildcat: weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) (2017)
Borji, A., Sihite, D.N., Itti, L.: Quantitative analysis of human-model agreement in visual saliency modeling: a comparative study. IEEE Trans. Image Process. 22(1), 55–69 (2013)
Mahasseni, B., Lam, M., Todorovic, S.: Unsupervised video summarization with adversarial lstm networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
Lindeberg, T.: Image matching using generalized scale-space interest points. J. Math. Imaging Vis. 52(1), 3–36 (2015)
Demirci, M.F., Platel, B., Shokoufandeh, A., Florack, L.L., Dickinson, S.J.: The representation and matching of images using top points. J. Math. Imaging Vis. 35(2), 103–116 (2009)
Itti, L., Rees, G., Tsotsos, J.K.: Neurobiology of Attention. Elsevier, Amsterdam (2005)
Deng, X., Zuo, F., Li, H.: Cracks detection using iterative phase congruency. J. Math. Imaging Vis. 60(7), 1065–1080 (2018)
Ahn, E., Kim, J., Bi, L., Kumar, A., Li, C., Fulham, M., Feng, D.D.: Saliency-based lesion segmentation via background detection in dermoscopic images. IEEE J. Biomed. Health Inf. 21(6), 1685–1693 (2017)
Wolfe, J.M., Cave, K.R., Franzel, S.L.: Guided search: an alternative to the feature integration model for visual search. J. Exp. Psychol. Hum. Percept. Perform. 15(3), 419 (1989)
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)
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)
Wang, L., Lu, H., Wang, Y., Feng, M., Wang, D., Yin, B., Ruan, X.: Learning to detect salient objects with image-level supervision. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), pp 136–145 (2017)
Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: IEEE 12th International Conference on Computer Vision, 2009, pp 2106–2113. IEEE (2009)
Kavak, Y., Erdem, E., Erdem, A.: A comparative study for feature integration strategies in dynamic saliency estimation. Signal Process. Image Commun. 51, 13–25 (2017)
Yang, J., Yang, M.H.: Top-down visual saliency via joint CRF and dictionary learning. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2296–2303. IEEE (2012)
Qi, J., Dong, S., Huang, F., Lu, H.: Saliency detection via joint modeling global shape and local consistency. Neurocomputing 222, 81–90 (2017)
Donoser, M., Urschler, M., Hirzer, M., Bischof, H.: Saliency driven total variation segmentation. In: 2009 IEEE 12th International Conference on Computer Vision, pp 817–824. IEEE (2009)
Zhu, C., Li, G., Wang, W., Wang, R.: An innovative salient object detection using center-dark channel prior. In: IEEE International Conference on Computer Vision Workshop (ICCVW) (2017)
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)
Zhang, J., Ehinger, K.A., Wei, H., Zhang, K., Yang, J.: A novel graph-based optimization framework for salient object detection. Pattern Recognit. 64, 39–50 (2017)
Huang, X., Zhang, Y.J.: 300-FPS salient object detection via minimum directional contrast. IEEE Trans. Image Process. 26(9), 4243–4254 (2017)
Oh, K., Lee, M., Kim, G., Kim, S.: Detection of multiple salient objects through the integration of estimated foreground clues. Image Vis. Comput. 54, 31–44 (2016)
Kienzle, W., Franz, M.O., Schölkopf, B., Wichmann, F.A.: Center-surround patterns emerge as optimal predictors for human saccade targets. J Vis. 9(5), 7–7 (2009)
Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814–2821 (2014)
Huang, K., Zhu, C., Li, G.: Robust saliency detection via fusing foreground and background priors. arXiv preprint arXiv:1711.00322 (2017)
Tu, W.C., He, S., Yang, Q., Chien, S.Y.: Real-time salient object detection with a minimum spanning tree. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2334–2342 (2016)
Zhang, J., Sclaroff, S.: Exploiting surroundedness for saliency detection: a boolean map approach. IEEE Trans. Pattern Anal. Mach. Intell. 38(5), 889–902 (2016)
Cheng, Y., Fu, H., Wei, X., Xiao, J., Cao, X.: Depth enhanced saliency detection method. In: Proceedings of International Conference on Internet Multimedia Computing and Service, p. 23. ACM (2014)
Alexe, B., Deselaers, T., Ferrari, V.: Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2189–2202 (2012)
Chikkerur, S., Serre, T., Tan, C., Poggio, T.: What and where: a bayesian inference theory of attention. Vis. Res. 50(22), 2233–2247 (2010)
Ren, J., Liu, Z., Zhou, X., Sun, G., Bai, C.: Saliency integration driven by similar images. J. Vis. Commun. Image Represent. 50, 227–236 (2018)
Gao, G., Han, C., Ma, K., Liu, C.H., Ding, G., Liu, E.: Optimal feature combination analysis for crowd saliency prediction. J. Vis. Commun. Image Represent. 50, 1–8 (2018)
Zeqiri, B.: Priming of visual attention in dynamic visual scenes-an experimental study using eye tracking. In: MEi: CogSci Conference 2013, Budapest (2013)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: CVPR 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1597–1604. IEEE (2009)
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, pp. 374–381. ACM (2003)
Itti, L., Baldi, P.: Bayesian surprise attracts human attention. Vis. Res. 49(10), 1295–1306 (2009)
Yu, Y., Choi, J., Kim, Y., Yoo, K., Lee, S.H., Kim, G.: Supervising neural attention models for video captioning by human gaze data. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, Hawaii, pp. 2680–2688 (2017)
Judd, T., Durand, F., Torralba, A.: A benchmark of computational models of saliency to predict human fixations, MIT Technical Report (2012)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Rother, C., Kolmogorov, V., Blake, A.: Grabcut: interactive foreground extraction using iterated graph cuts. In: ACM Transactions on Graphics (TOG), vol 23, pp. 309–314. ACM (2004)
Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Salient object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 1, 1–1 (2014)
Tepper, M., Musé, P., Almansa, A.: On the role of contrast and regularity in perceptual boundary saliency. J. Math. Imaging Vis. 48(3), 396–412 (2014)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006)
Ahn, E., Lee, S., Kim, G.J.: Real-time adjustment of contrast saliency for improved information visibility in mobile augmented reality. Virtual Real. 22(3), 245–262 (2018)
Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: CVPR’07 IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1–8. IEEE (2007)
Lv, Q., Wang, B., Zhang, L.: Saliency computation via whitened frequency band selection. Cogn. Neurodyn. 10(3), 255–267 (2016)
Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: International Conference on Computer Vision Systems, pp. 66–75. Springer (2008)
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)
Huang, X., Zhang, Y.: Water flow driven salient object detection at 180 fps. Pattern Recognit. 76, 95–107 (2018)
Borji, A., Cheng, M.M., Hou, Q., Jiang, H., Li, J.: Salient object detection: a survey. arXiv preprint arXiv:1411.5878 (2014)
Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706–5722 (2015)
Achanta, R., Süsstrunk, S.: Saliency detection using maximum symmetric surround. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 2653–2656. IEEE (2010)
Liu, F., Gleicher, M.: Region enhanced scale-invariant saliency detection. In: 2006 IEEE International Conference on Multimedia and Expo, pp. 1477–1480. IEEE (2006)
Perazzi, F., Krähenbühl, 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)
Cheng, M.M., Warrell, J., Lin, W.Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1529–1536. IEEE (2013)
Zhang, L., Yang, C., Lu, H., Ruan, X., Yang, M.H.: Ranking saliency. IEEE Trans. Pattern Anal. Mach. Intell. 39(9), 1892–1904 (2017)
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)
Zhang, L., Ai, J., Jiang, B., Lu, H., Li, X.: Saliency detection via absorbing Markov chain with learnt transition probability. IEEE Trans. Image Process. 27(2), 987–998 (2018)
Shi, J., Yan, Q., Xu, L., Jia, J.: Hierarchical image saliency detection on extended CSSD. IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 717–729 (2016)
Qin, Y., Lu, H., Xu, Y., Wang, H.: Saliency detection via cellular automata. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 110–119. IEEE (2015)
Cheng, G., Han, J., Zhou, P., Xu, D.: Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection. IEEE Trans. Image Process. 28(1), 265–278 (2019)
Zhao, R., Ouyang, W., Li, H., Wang, X.: Saliency detection by multi-context deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265–1274 (2015)
Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., Torr, P.H.: Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3203–3212 (2017)
Dong, S., Gao, Z., Sun, S., Wang, X., Li, M., Zhang, H., Yang, G., Liu, H., Li, S.: Holistic and deep feature pyramids for saliency detection. In: British Machine Vision Conference (BMVC), pp. 3–6. Northumbria University, Newcastle (2018)
Wang, T., Borji, A., Zhang, L., Zhang, P., Lu, H.: A stagewise refinement model for detecting salient objects in images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4019–4028 (2017)
Han, J., Chen, H., Liu, N., Yan, C., Li, X.: CNNs-based RGB-D saliency detection via cross-view transfer and multiview fusion. IEEE Trans. Cybern. 99, 1–13 (2017)
Li, M., Dong, S., Zhang, K., Gao, Z., Wu, X., Zhang, H., Yang, G., Li, S.: Deep learning intra-image and inter-images features for co-saliency detection (2018)
Han, J., Cheng, G., Li, Z., Zhang, D.: A unified metric learning-based framework for co-saliency detection. IEEE Trans. Circuits Syst. Video Technol. 28(10), 2473–2483 (2018)
Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Trans. Graph. (TOG) 22(3), 313–318 (2003)
Harremoës, P.: Binomial and Poisson distributions as maximum entropy distributions. IEEE Trans. Inf. Theory 47(5), 2039–2041 (2001)
Kourtzi, Z., Kanwisher, N.: Representation of perceived object shape by the human lateral occipital complex. Science 293(5534), 1506–1509 (2001)
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), 32–32 (2008)
Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. Georgia Institute of Technology, Atlanta (2014)
Li, X., Li, Y., Shen, C., Dick, A., Van Den Hengel, A.: Contextual hypergraph modeling for salient object detection. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 3328–3335. IEEE (2013a)
Li, J., Levine, M.D., An, X., Xu, X., He, H.: Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 996–1010 (2013b)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Singh, S.K., Srivastava, R. A Novel Probabilistic Contrast-Based Complex Salient Object Detection. J Math Imaging Vis 61, 990–1006 (2019). https://doi.org/10.1007/s10851-019-00882-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10851-019-00882-3