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DenseASPP Enriched Residual Network Towards Visual Saliency Prediction

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Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1568))

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Abstract

Predicting regions of interest, otherwise called salient regions tend to top out with the rise of deep learning techniques. Although convolutional neural networks have evaded the domain to let it reach newer heights, there still exists room for improvement on how to integrate the hierarchical features efficiently. In fact, the rich features at multiple spatial scales are found to be powerful towards accurate prediction. This paper proposes a novel end-to-end visual saliency prediction technique, based on DenseASPP (Dense Atrous Spatial Pyramid Pooling) and residual connections. It enriches the multi-scale contextual features via DenseASPP module, that gathers information via dense connections across multiple scales. Further, incorporation of residual connections between encoder and decoder blocks allow learning of more robust features that can result in better prediction. The model is trained on the largest dataset for saliency prediction, SALICON and experimental results on two public datasets, OSIE and PASCAL-S verify the effectiveness of the proposed framework compared with state-of-the-art results.

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References

  1. Bruce, N., Tsotsos, J.: Saliency based on information maximization. In: Advances in Neural Information Processing Systems, pp. 155–162 (2005)

    Google Scholar 

  2. Bruce, N.D., Catton, C., Janjic, S.: A deeper look at saliency: feature contrast, semantics, and beyond. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 516–524. IEEE, Las Vegas (2016)

    Google Scholar 

  3. Bruce, N.D., Tsotsos, J.K.: Saliency, attention, and visual search: an information theoretic approach. J. Vis. 9(3), 5 (2009)

    Article  Google Scholar 

  4. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional Nets, Atrous Convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  5. Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)

  6. Cornia, M., Baraldi, L., Serra, G., Cucchiara, R.: A deep multi-level network for saliency prediction. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3488–3493. IEEE (2016)

    Google Scholar 

  7. Cornia, M., Baraldi, L., Serra, G., Cucchiara, R.: Predicting human eye fixations via an LSTM-based saliency attentive model. IEEE Trans. Image Process. 27(10), 5142–5154 (2018)

    Article  MathSciNet  Google Scholar 

  8. Dodge, S.F., Karam, L.J.: Visual saliency prediction using a mixture of deep neural networks. IEEE Trans. Image Process. 27(8), 4080–4090 (2018)

    Article  MathSciNet  Google Scholar 

  9. Erdem, E., Erdem, A.: Visual saliency estimation by nonlinearly integrating features using region covariances. J. Vis. 13(4), 11–11 (2013)

    Article  Google Scholar 

  10. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Article  Google Scholar 

  11. Garcia-Diaz, A., Leboran, V., Fdez-Vidal, X.R., Pardo, X.M.: On the relationship between optical variability, visual saliency, and eye fixations: a computational approach. J. Vis. 12(6), 17 (2012)

    Article  Google Scholar 

  12. Ghariba, B.M., Shehata, M.S., McGuire, P.: A novel fully convolutional network for visual saliency prediction. PeerJ. Comput. Sci. 6, e280 (2020)

    Article  Google Scholar 

  13. Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2011)

    Article  Google Scholar 

  14. Harel, J., Koch, C., Perona, P.: Graph-based visual saliency (2007)

    Google Scholar 

  15. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  16. He, S., Borji, A., Mi, Y., Pugeault, N.: What catches the eye? Visualizing and understanding deep saliency models. arXiv preprint arXiv:1803.05753 (2018)

  17. 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)

    Article  Google Scholar 

  18. Jiang, L., Wang, Z., Xu, M., Wang, Z.: Image saliency prediction in transformed domain: a deep complex neural network method. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8521–8528 (2019)

    Google Scholar 

  19. Jiang, M., Huang, S., Duan, J., Zhao, Q.: SALICON: saliency in context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1072–1080 (2015)

    Google Scholar 

  20. Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2106–2113. IEEE (2009)

    Google Scholar 

  21. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  22. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2012)

    Google Scholar 

  23. Kroner, A., Senden, M., Driessens, K., Goebel, R.: Contextual encoder-decoder network for visual saliency prediction. Neural Netw. 129, 261–270 (2020)

    Article  Google Scholar 

  24. Kruthiventi, S.S., Ayush, K., Babu, R.V.: DeepFix: a fully convolutional neural network for predicting human eye fixations. IEEE Trans. Image Process. 26(9), 4446–4456 (2017)

    Article  MathSciNet  Google Scholar 

  25. Kruthiventi, S.S., Gudisa, V., Dholakiya, J.H., Babu, R.V.: Saliency unified: a deep architecture for simultaneous eye fixation prediction and salient object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5781–5790 (2016)

    Google Scholar 

  26. Kümmerer, M., Theis, L., Bethge, M.: Deep Gaze I: boosting saliency prediction with feature maps trained on ImageNet. arXiv preprint arXiv:1411.1045 (2014)

  27. Li, P., Xing, X., Xu, X., Cai, B., Cheng, J.: Attention-aware concentrated network for saliency prediction. Neurocomputing 429, 199–214 (2021)

    Article  Google Scholar 

  28. 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)

    Google Scholar 

  29. Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  30. Liu, N., Han, J.: A deep spatial contextual long-term recurrent convolutional network for saliency detection. IEEE Trans. Image Process. 27(7), 3264–3274 (2018)

    Article  MathSciNet  Google Scholar 

  31. Liu, N., Han, J., Zhang, D., Wen, S., Liu, T.: Predicting eye fixations using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 362–370 (2015)

    Google Scholar 

  32. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  33. Pan, J., Sayrol, E., Giro-i Nieto, X., McGuinness, K., O’Connor, N.E.: Shallow and deep convolutional networks for saliency prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 598–606 (2016)

    Google Scholar 

  34. Reddy, N., Jain, S., Yarlagadda, P., Gandhi, V.: Tidying deep saliency prediction architectures. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10241–10247. IEEE (2020)

    Google Scholar 

  35. Ren, D., Wen, X., Jia, T., Chen, J., Li, Z.: Saliency detection via cross-scale deep inference. J. Vis. Commun. Image Represent. 75, 103031 (2021)

    Article  Google Scholar 

  36. Riche, N., Duvinage, M., Mancas, M., Gosselin, B., Dutoit, T.: Saliency and human fixations: state-of-the-art and study of comparison metrics. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1153–1160 (2013)

    Google Scholar 

  37. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  38. Szegedy, C.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  39. Vig, E., Dorr, M., Cox, D.: Large-scale optimization of hierarchical features for saliency prediction in natural images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2798–2805 (2014)

    Google Scholar 

  40. Wang, W., Shen, J.: Deep visual attention prediction. IEEE Trans. Image Process. 27(5), 2368–2378 (2017)

    Article  MathSciNet  Google Scholar 

  41. Wang, Z., Liu, Z., Wei, W., Duan, H.: SALED: saliency prediction with a pithy encoder-decoder architecture sensing local and global information. Image Vis. Comput. 109, 104149 (2021)

    Article  Google Scholar 

  42. Xu, J., Jiang, M., Wang, S., Kankanhalli, M.S., Zhao, Q.: Predicting human gaze beyond pixels. J. Vis. 14(1), 28–28 (2014)

    Article  Google Scholar 

  43. Yang, M., Yu, K., Zhang, C., Li, Z., Yang, K.: DenseASPP for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3684–3692 (2018)

    Google Scholar 

  44. Yang, S., Lin, G., Jiang, Q., Lin, W.: A dilated inception network for visual saliency prediction. IEEE Trans. Multimedia 22(8), 2163–2176 (2019)

    Article  Google Scholar 

  45. Zabihi, S., Tavakoli, H.R., Borji, A.: A compact deep architecture for real-time saliency prediction. arXiv preprint arXiv:2008.13227 (2020)

  46. Zhang, J., Sclaroff, S.: Saliency detection: a Boolean map approach. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 153–160 (2013)

    Google Scholar 

  47. 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)

    Article  Google Scholar 

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Authors and Affiliations

  1. Division of Information Technology, Cochin University of Science and Technology, Kerala, India

    Shilpa Elsa Abraham & Binsu C. Kovoor

Authors
  1. Shilpa Elsa Abraham
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  2. Binsu C. Kovoor
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Correspondence to Shilpa Elsa Abraham .

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Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

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Abraham, S.E., Kovoor, B.C. (2022). DenseASPP Enriched Residual Network Towards Visual Saliency Prediction. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-11349-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11348-2

  • Online ISBN: 978-3-031-11349-9

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