Abstract
Saliency or the salient region changes in the human vision system depending on the type of its behavior and task. That is, the salient region in human vision system is not independent, but dependent on other parameters. If a saliency detection algorithm intends to work like the human vision system, it must have an input as its vision in order to detect that salient region or salient object according to that input. The proposed algorithm of this article (Salient Object Detection using Task Simulation based on Angle) is indeed the updated version of SOD-TS algorithm. In this method we have tried to simulate the task as an angle parameter in order to be able to detect the object independent of its size and rotation. In our proposed method, the algorithm detects the most salient object with regard to the applied task. This method can be used in detecting salient objects, detecting different types of ships, and different types of airplanes, and in edge detection. One of the most important advantages of this approach is its very high speed.
Similar content being viewed by others
References
Achanta R, et al. (2008) Salient region detection and segmentation. International conference on computer vision systems. Springer, Berlin, Heidelberg
Achanta R, Süsstrunk S (2010) Saliency detection using maximum symmetric surround." 2010 IEEE International Conference on Image Processing. IEEE, 2010.
Achanta R, et al. (2009) Frequency-tuned salient region detection. 2009 IEEE conference on computer vision and pattern recognition. IEEE, 2009.
Achanta R, et al. (2009) Frequency-tuned salient region detection. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2009). No CONF 2009.
Afsharirad, Hooman, and Seyed Alireza Seyedin (2019) Salient object detection using the phase information and object model. Multimed Tools Appl : 1–20.
Alexe B, Deselaers T, Ferrari V (2010) What is an object? 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, 2010
Aytekin C, Serkan Kiranyaz, and Moncef Gabbouj (2014) Automatic object segmentation by quantum cuts. 22nd International Conference on Pattern Recognition. IEEE 2014.
Bernstein DA (2010) Essentials of psychology. Cengage Learning : 123–124
Borji A, Itti L (2012) State-of-the-art in visual attention modeling. IEEE Trans Pattern Anal Mach Intell 35(1):185–207
Borji A, Sihite DN, Itti L (2013) What stands out in a scene? A study of human explicit saliency judgment. Vis Res 91:62–77
Borji A, Cheng M-M, Jiang H, Li J (2015) Salient object detection: a benchmark. IEEE Trans Image Process 24(12):5706–5722
Chang K-Y, et al. (2011) Fusing generic objectness and visual saliency for salient object detection." 2011 International Conference on Computer Vision. IEEE
Cheng M-M, et al. (2013) Efficient salient region detection with soft image abstraction. Proceedings of the IEEE International Conference on Computer vision. 2013.
Cheng M-M et al (2014) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569–582
Duan L, et al. (2011) Visual saliency detection by spatially weighted dissimilarity. CVPR 2011. IEEE, 2011.
ECSSD http://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html
Goferman S, Zelnik-Manor L, Tal A (2011) Context-aware saliency detection. IEEE Trans Pattern Anal Mach Intell 34(10):1915–1926
Gong C, et al. (2015) Saliency propagation from simple to difficult. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Harel J, Koch C, Perona P (2007) Graph-based visual saliency. Adv Neural Inf Proces Syst
Hou X, Zhang L (2007) Saliency detection: A spectral residual approach. 2007 IEEE conference on computer vision and pattern recognition. Ieee, 2007.
Huang R, Feng W, Sun J (2017) Color feature reinforcement for cosaliency detection without single saliency residuals. IEEE Signal Process Lett 24(5):569–573
Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis & Machine Intelligence 11:1254–1259
Jian M, Lam KM, Dong J, Shen L (2014) Visual-patch-attention-aware saliency detection. IEEE Trans Cybern 45(8):1575–1586
Jian M, Zhang W, Yu H, Cui C, Nie X, Zhang H, Yin Y (2018) Saliency detection based on directional patches extraction and principal local color contrast. J Vis Commun Image Represent 57:1–11
Jian M, Zhao R, Sun X, Luo H, Zhang W, Zhang H, Dong J, Yin Y, Lam K-M (2018) Saliency detection based on background seeds by object proposals and extended random walk. J Vis Commun Image Represent 57:202–211
Jian M, Qi Q, Dong J, Sun X, Sun Y, Lam K-M (2018) Saliency detection using quaternionic distance based weber local descriptor and level priors. Multimed Tools Appl 77(11):14343–14360
Jian M, Zhou Q, Cui C, Nie X, Luo H, Zhao J, Yin Y (2019) Assessment of feature fusion strategies in visual attention mechanism for saliency detection. Pattern Recogn Lett 127:37–47
Jiang H, et al. (2011) Automatic salient object segmentation based on context and shape prior. BMVC. 6(7)
Jiang, Huaizu, et al. (2013) Salient object detection: A discriminative regional feature integration approach. Proceedings of the IEEE conference on computer vision and pattern recognition
Jiang, Peng, et al. (2013) Salient region detection by ufo: Uniqueness, focusness and objectness. Proceedings of the IEEE international conference on computer vision
Judd, Tilke, et al. (2009) Learning to predict where humans look. 2009 IEEE 12th international conference on computer vision. IEEE.
Kim J, et al. (2014) Salient region detection via high-dimensional color transform. Proceedings of the IEEE conference on computer vision and pattern recognition
Li X, et al. (2013) Saliency detection via dense and sparse reconstruction. Proceedings of the IEEE international conference on computer vision
Li, Xi, et al. (2013) Contextual hypergraph modeling for salient object detection. Proceedings of the IEEE international conference on computer vision
Li C, et al. (2015) Robust saliency detection via regularized random walks ranking. Proceedings of the IEEE conference on computer vision and pattern recognition
Li H, Lu H, Lin Z, Shen X, Price B (2015) Inner and inter label propagation: salient object detection in the wild. IEEE Trans Image Process 24(10):3176–3186
Liu T et al (2010) Learning to detect a salient object. IEEE Trans Pattern Anal Mach Intell 33(2):353–367
Liu Z, Zou W, Le Meur O (2014) Saliency tree: a novel saliency detection framework. IEEE Trans Image Process 23(5):1937–1952
Lu Y et al (2019) A novel multi-graph framework for salient object detection. Vis Comput:1–17
Lu Y, Zhou K, Wu X, Gong P (2019) A novel multi-graph framework for salient object detection. Vis Comput 35(11):1683–1699
Margolin R, Tal A, Zelnik-Manor L (2013) What makes a patch distinct? Proc IEEE Conf Comput Vis Pattern Recognit
Margolin R, Zelnik-Manor L, Tal A (2013) Saliency for image manipulation. Vis Comput 29(5):381–392
Margolin R, Zelnik-Manor L, Tal A (2014) How to evaluate foreground maps? Proc IEEE Conf Comput Vis Pattern Recognit
Movahedi, Vida, and James H. Elder. (2010) Design and perceptual validation of performance measures for salient object segmentation. IEEE computer society conference on computer vision and pattern recognition-workshops. IEEE, 2010.
MSRA10K https://mmcheng.net
Parkhurst D, Law K, Niebur E (2002) Modeling the role of salience in the allocation of overt visual attention. Vis Res 42(1):107–123
PASCALS https://mmcheng.net
Peng H, et al. (2013) Salient object detection via low-rank and structured sparse matrix decomposition. Twenty-Seventh AAAI Conference on Artificial Intelligence
Perazzi, Federico, et al. (2012) Saliency filters: Contrast based filtering for salient region detection. 2012 IEEE conference on computer vision and pattern recognition. IEEE, 2012
Qi W, Cheng M-M, Borji A, Lu H, Bai L-F (2015) SaliencyRank: two-stage manifold ranking for salient object detection. Comput Vis Media 1(4):309–320
Qin Y, et al. (2015) Saliency detection via cellular automata. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Qin X, et al. (2019) Basnet: Boundary-aware salient object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Rahtu E, et al. (2010) Segmenting salient objects from images and videos. European conference on computer vision. Springer, Berlin, Heidelberg
Siva P, et al. (2013) Looking beyond the image: Unsupervised learning for object saliency and detection. Proceedings of the IEEE conference on computer vision and pattern recognition
Tang C et al (2016) Salient object detection via weighted low rank matrix recovery. IEEE Signal Processing Letters 24(4):490–494
Tavakoli HR, Esa Rahtu, and Janne Heikkilä (2011) Fast and efficient saliency detection using sparse sampling and kernel density estimation. Scandinavian conference on image analysis. Springer, Berlin, Heidelberg
THUR15K https://mmcheng.net
Ullah I, (2020) et al. A brief survey of visual saliency detection. Multimed Tools Appl : 1–41.
Wang X et al (2017) Edge preserving and multi-scale contextual neural network for salient object detection. IEEE Trans Image Process 27(1):121–134
Wei, Yichen, Fang Wen, and Jian Sun (2017) Geodesic saliency using background priors. U.S. Patent No. 9,697,612. 4 Jul.
Xie Y, Lu H, Yang M-H (2012) Bayesian saliency via low and mid level cues. IEEE Trans Image Process 22(5):1689–1698
Yan Q, et al. (2013) Hierarchical saliency detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Yang C, et al. (2013) Saliency detection via graph-based manifold ranking. Proceedings of the IEEE conference on computer vision and pattern recognition
Yang J, Yang M-H (2016) Top-down visual saliency via joint CRF and dictionary learning. IEEE Trans Pattern Anal Mach Intell 39(3):576–588
Yang C, Zhang L, Huchuan L (2013) Graph-regularized saliency detection with convex-hull-based center prior. IEEE Signal Process Lett 20(7):637–640
Zhai, Yun, and Mubarak Shah (2006) Visual attention detection in video sequences using spatiotemporal cues. Proceedings of the 14th ACM international conference on Multimedia. ACM
Zhang, Xiaoning, et al. (2018) Progressive attention guided recurrent network for salient object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Zhang L et al (2017) Saliency detection via absorbing Markov chain with learnt transition probability. IEEE Trans Image Process 27(2):987–998
Zhong, Zhun, et al. (2017) Re-ranking person re-identification with k-reciprocal encoding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Zhou L, Yang Z, Yuan Q, Zhou Z, Hu D (2015) Salient region detection via integrating diffusion-based compactness and local contrast. IEEE Trans Image Process 24(11):3308–3320
Zhu W, et al. (2014) Saliency optimization from robust background detection. Proceedings of the IEEE conference on computer vision and pattern recognition
Zou W, Komodakis N (2015) Harf: hierarchy-associated rich features for salient object detection. Proceedings of the IEEE international conference on computer vision
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
Afsharirad, H. Salient object detection using task simulation as a new input. Multimed Tools Appl 80, 8689–8719 (2021). https://doi.org/10.1007/s11042-020-09933-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09933-z