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Fusion hierarchy motion feature for video saliency detection

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Abstract

Saliency detection plays an important role in computer vision and scene understanding, which has attracted increasing attention in recent years. Compared to the widely studied image saliency prediction, there are still many problems to be solved in the area of video saliency. Different from images, effectively describing and utilizing the motion information contained in video data is a critical issue. In this paper, we propose a spatial and motion dual-stream framework for video saliency detection. The coarse motion features extracting from optical flow are fine-tuned with higher level semantic spatial features via a residual cross-connection. A hierarchical fusion structure is proposed to maintain contextual information by integrating spatial and motion features in each level. To model the inter-frame correlation in the video, the convolutional gated recurrent unit (convGRU) is used to retain global consistency of the saliency area between neighbor frames. Experimental results on four widely used datasets demonstrate the effectiveness of the proposed method with other state-of-the-art methods. Our source codes can be acquired at https://github.com/banhuML/MFHF.

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Data Availibility

The datasets generated during and/or analysed during the current study are available in the DHF1K repository, https://github.com/wenguanwang/DHF1K.

References

  1. Wang X, Qi C (2020) Detecting action-relevant regions for action recognition using a three-stage saliency detection technique. Multimed Tools Appl 79(11):7413–7433

    Article  Google Scholar 

  2. Cizmeciler K, Erdem E, Erdem A (2022) Leveraging semantic saliency maps for query-specific video summarization. Multimed Tools Appl 81(12):17457–17482

    Article  Google Scholar 

  3. Ullah J, Khan A, Jaffar MA (2018) Motion cues and saliency based unconstrained video segmentation. Multimed Tools Appl 77(6):7429–7446

    Article  Google Scholar 

  4. Li S, Xu M, Wang Z, Sun X (2016) Optimal bit allocation for ctu level rate control in hevc. IEEE Trans Circ Syst Video Technol 27(11):2409–2424

    Article  Google Scholar 

  5. Xu M, Liu Y, Hu R, He F (2018) Find who to look at: turning from action to saliency. IEEE Trans Image Proc 27(9):4529–4544

    Article  ADS  MathSciNet  Google Scholar 

  6. Chen C, Li S, Wang Y, Qin H, Hao A (2017) Video saliency detection via spatial-temporal fusion and low-rank coherency diffusion. IEEE Trans Image Proc 26(7):3156–3170

    Article  ADS  MathSciNet  Google Scholar 

  7. Chen C, Li S, Qin H, Pan Z, Yang G (2018) Bilevel feature learning for video saliency detection. IEEE Trans Multimed 20(12):3324–3336

    Article  Google Scholar 

  8. Li Y, Li S, Chen C, Hao A, Qin H (2019) Accurate and robust video saliency detection via self-paced diffusion. IEEE Trans Multimed 22(5):1153–1167

    Article  Google Scholar 

  9. Chen C, Wang G, Peng C, Zhang X, Qin H (2019) Improved robust video saliency detection based on long-term spatial-temporal information. IEEE Trans Image Proc 29:1090–1100

    Article  ADS  MathSciNet  Google Scholar 

  10. Zhang P, Liu J, Wang X, Pu T, Fei C, Guo Z (2020) Stereoscopic video saliency detection based on spatiotemporal correlation and depth confidence optimization. Neurocomputing 377:256–268

    Article  Google Scholar 

  11. Wang G, Chen C, Fan D, Hao A, Qin H (2021) Weakly supervised visual-auditory saliency detection with multigranularity perception. arXiv preprint arXiv:2112.13697

  12. Li H, Chen G, Li G, Yu Y (2019) Motion guided attention for video salient object detection. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 7274–7283

  13. Chen C, Song J, Peng C, Wang G, Fang Y (2021) A novel video salient object detection method via semisupervised motion quality perception. IEEE Trans Circ Syst Video Technol 32(5):2732–2745

    Article  Google Scholar 

  14. Chen C, Wang H, Fang Y, Peng C (2022) A novel long-term iterative mining scheme for video salient object detection. IEEE Trans Circ Syst Video Technol 32(11):7662–7676

    Article  Google Scholar 

  15. Borji A, Cheng M-M, Jiang H, Li J (2015) Salient object detection: a benchmark. IEEE Trans Image Proc 24(12):5706–5722

    Article  ADS  MathSciNet  Google Scholar 

  16. Vig E, Dorr M, Cox D (2014) 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

  17. Liu N, Han J, Zhang D, Wen S, Liu T (2015) Predicting eye fixations using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 362–370

  18. Huang X, Shen C, Boix X, Zhao Q (2015) Salicon: reducing the semantic gap in saliency prediction by adapting deep neural networks. In: Proceedings of the IEEE international conference on computer vision, pp 262–270

  19. Pan J, Sayrol E, Giro-i-Nieto X, McGuinness K, O’Connor NE (2016) Shallow and deep convolutional networks for saliency prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 598–606

  20. Liu N, Han J, Liu T, Li X (2016) Learning to predict eye fixations via multiresolution convolutional neural networks. IEEE Trans Neur Netw Learn Syst 29(2):392–404

    Article  MathSciNet  CAS  Google Scholar 

  21. Kruthiventi SS, Ayush K, Babu RV (2017) Deepfix: a fully convolutional neural network for predicting human eye fixations. IEEE Trans Image Proc 26(9):4446–4456

    Article  ADS  MathSciNet  Google Scholar 

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

    Article  ADS  MathSciNet  Google Scholar 

  23. Mathe S, Sminchisescu C (2014) Actions in the eye: dynamic gaze datasets and learnt saliency models for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(7):1408–1424

    Article  Google Scholar 

  24. Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259

    Article  Google Scholar 

  25. Gibson JJ (1950) The perception of the visual world

  26. Teed Z, Deng J (2020) Raft: recurrent all-pairs field transforms for optical flow. In: European conference on computer vision. Springer, pp 402–419

  27. Cong R, Song W, Lei J, Yue G, Zhao Y, Kwong S (2022) Psnet: parallel symmetric network for video salient object detection. IEEE Trans Emerg Top Comput Intell

  28. Bak C, Kocak A, Erdem E, Erdem A (2017) Spatio-temporal saliency networks for dynamic saliency prediction. IEEE Trans Multimed 20(7):1688–1698

    Article  Google Scholar 

  29. Jiang L, Xu M, Liu T, Qiao M, Wang Z (2018) Deepvs: a deep learning based video saliency prediction approach. In: Proceedings of the European conference on computer vision (eccv), pp 602–617

  30. Zhang K, Chen Z (2018) Video saliency prediction based on spatial-temporal two-stream network. IEEE Trans Circ Syst Video Technol 29(12):3544–3557

    Article  Google Scholar 

  31. Lai Q, Wang W, Sun H, Shen J (2019) Video saliency prediction using spatiotemporal residual attentive networks. IEEE Trans Image Proc 29:1113–1126

    Article  ADS  MathSciNet  Google Scholar 

  32. Ballas N, Yao L, Pal C, Courville A (2015) Delving deeper into convolutional networks for learning video representations. arXiv preprint arXiv:1511.06432

  33. Srinivasu PN, Bhoi AK, Jhaveri RH, Reddy GT, Bilal M (2021) Probabilistic deep q network for real-time path planning in censorious robotic procedures using force sensors. J Real-Time Image Proc 18(5):1773–1785

    Article  Google Scholar 

  34. Craye C, Filliat D, Goudou J-F (2016) Environment exploration for object-based visual saliency learning. In: 2016 IEEE international conference on robotics and automation (ICRA), pp 2303–2309. IEEE

  35. Le Meur O, Le Callet P, Barba D, Thoreau D (2006) A coherent computational approach to model bottom-up visual attention. IEEE Trans Pattern Ana Mach Intell 28(5):802–817

    Article  Google Scholar 

  36. Zhang L, Tong MH, Marks TK, Shan H, Cottrell GW (2008) Sun: a bayesian framework for saliency using natural statistics. J Vision 8(7):32–32

    Article  Google Scholar 

  37. Gao D, Vasconcelos N (2005) Discriminant saliency for visual recognition from cluttered scenes. In: Adv Neural Inf Proc Syst, pp 481–488

  38. Bruce N, Tsotsos J (2005) Saliency based on information maximization. Adv Neural Inf Proc Syst 18:155–162

    Google Scholar 

  39. Cheng M-M, Mitra NJ, Huang X, Torr PH, Hu S-M (2014) Global contrast based salient region detection. IEEE Trans Pattern Anal Machi Intell 37(3):569–582

    Article  Google Scholar 

  40. Xu M, Ren Y, Wang Z (2015) Learning to predict saliency on face images. In: Proceedings of the IEEE international conference on computer vision, pp 3907–3915

  41. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Article  Google Scholar 

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

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

    Article  ADS  MathSciNet  Google Scholar 

  44. Shi X, Chen Z, Wang H, Yeung D-Y, Wong W-K, Woo W-c (2015) Convolutional lstm network: a machine learning approach for precipitation nowcasting. Adv Neural Inf Proc Syst 28

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

    Article  ADS  MathSciNet  Google Scholar 

  46. Guo C, Ma Q, Zhang L (2008) Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: 2008 IEEE conference on computer vision and pattern recognition, pp 1–8 IEEE

  47. Itti L, Dhavale N, Pighin F (2003) Realistic avatar eye and head animation using a neurobiological model of visual attention. In: Applications and science of neural networks, fuzzy systems, and evolutionary computation VI, vol 5200, pp 64–78. SPIE

  48. Wang W, Shen J, Xie J, Cheng M-M, Ling H, Borji A (2019) Revisiting video saliency prediction in the deep learning era. IEEE Trans Pattern Anal Mach Intell 1–1. https://doi.org/10.1109/TPAMI.2019.2924417

  49. Zhu S, Chang Q, L, Q (2022) Video saliency aware intelligent hd video compression with the improvement of visual quality and the reduction of coding complexity. Neural Computing and Applications 1–20

  50. Chen C, Wang G, Peng C, Fang Y, Zhang D, Qin H (2021) Exploring rich and efficient spatial temporal interactions for real-time video salient object detection. IEEE Trans Image Proc 30:3995–4007

    Article  ADS  Google Scholar 

  51. Zhang F, Woodford OJ, Prisacariu VA, Torr PH (2021) Separable flow: Learning motion cost volumes for optical flow estimation. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 10807–10817

  52. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788

  53. Dosovitskiy A, Fischer P, Ilg E, Hausser P, Hazirbas C, Golkov V, Van Der Smagt P, Cremers D, Brox T (2015) Flownet: learning optical flow with convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 2758–2766

  54. Mital P, mith TJ, Luke S, Henderson J (2013) Do low-level visual features have a causal influence on gaze during dynamic scene viewing? J Vision 13(9):144–144

  55. Abrams RA (2003) Christ SE (2003) Motion onset captures attention. Psychol Sci 14(5):427–432

    Article  PubMed  Google Scholar 

  56. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  57. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8): 1735–1780

  58. Mital PK, Smith TJ, Hill RL, Henderson JM (2011) Clustering of gaze during dynamic scene viewing is predicted by motion. Cogn Comput 3(1):5–24

    Article  Google Scholar 

  59. Judd T, Ehinger K, Durand F, Torralba A (2009) Learning to predict where humans look. In: 2009 IEEE 12th international conference on computer vision, pp 2106–2113. IEEE

  60. Borji A, Tavakoli HR, Sihite DN, Itti L (2013) Analysis of scores, datasets, and models in visual saliency prediction. In: Proceedings of the IEEE international conference on computer vision, pp 921–928

  61. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  62. Linardos P, Mohedano E, Nieto JJ, O’Connor NE, Giro-i-Nieto X, McGuinness K (2019) Simple vs complex temporal recurrences for video saliency prediction. arXiv preprint arXiv:1907.01869

  63. Min K, Corso JJ (2019) Tased-net: temporally-aggregating spatial encoder-decoder network for video saliency detection. In: Proceedings of the IEEE international conference on computer vision, pp 2394–2403

  64. Wang Z, Zhou Z, Lu H, Hu Q, Jiang J (2020) Video saliency prediction via joint discrimination and local consistency. IEEE Transactions on Cybernetics

  65. Wang Z, Zhou Z, Lu H, Jiang J (2020) Global and local sensitivity guided key salient object re-augmentation for video saliency detection. Pattern Recogn 103:107275

    Article  Google Scholar 

  66. Jiang L, Xu M, Zhang S, Sigal L (2020) Deepct: a novel deep complex-valued network with learnable transform for video saliency prediction. Pattern Recogn 102:107234

  67. Zou W, Zhuo S, Tang Y, Tian S, Li X, Xu C (2021) Sta3d: spatiotemporally attentive 3d network for video saliency prediction. Pattern Recognition Letters 147:78–84

    Article  ADS  Google Scholar 

  68. Xue H, Sun M, Liang Y (2022) Ecanet: explicit cyclic attention-based network for video saliency prediction. Neurocomput 468:233–244

    Article  Google Scholar 

  69. Chen J, Li Z, Jin Y, Ren D, Ling H (2021) Video saliency prediction via spatio-temporal reasoning. Neurocomput 462:59–68

    Article  Google Scholar 

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Acknowledgements

This research was supported by the National Science and Technology Major Project (Grant No. 2020YFA0713504), the National Natural Science Foundation of China (Nos. 62376238, 62372170), and the Scientific Research Foundation of Education Department of Hunan Province of China (Grant Nos. 21A0109)

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Authors and Affiliations

  1. The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, Hunan, China

    Fen Xiao, Huiyu Luo, Wenlei Zhang & Zhen Li

  2. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, Hunan, China

    Xieping Gao

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  1. Fen Xiao
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Xiao, F., Luo, H., Zhang, W. et al. Fusion hierarchy motion feature for video saliency detection. Multimed Tools Appl 83, 32301–32320 (2024). https://doi.org/10.1007/s11042-023-16593-2

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