Abstract
Salient object detection aims to emulate the extraordinary capability of human visual system, which has the ability to find the most visually attractive objects in a complex visual scene. The human visual attention is often complicated and affected by many factors. In this paper, we present a novel bottom-up approach to automatically detect salient objects of an image via multiple visual cues. The key idea of our approach is to represent a saliency map of an image as an integration of multiple visual cues (saliency weights), which have been proven to be effective and useful. Specifically, we propose four saliency weights, i.e., local contrast weight, superpixel clarity weight, background probability weight, and central bias weight, to effectively represent each visual cue. To obtain our saliency map, the four resulting saliency weights are integrated in a principled way via multiplication and summation based fusion. Furthermore, we propose a new superpixel-level saliency smoothing approach to optimize the integrated results for producing clean and consistent saliency maps. Our experimental results on three standard benchmark datasets demonstrate that the proposed approach outperforms other saliency detection approaches in terms of the subjective observations and objective evaluations.
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References
Achanta R, Estrada F, Wils P, Ssstrunk S (2008) Salient region detection and segmentation. In: Computer vision systems, vol 5008, pp 66–75
Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: IEEE Conference on computer vision and pattern recognition (CVPR’2009), pp 1597–1604
Alfano PL, Michel GF (1990) Restricting the field of view: perceptual and performance effects. Percept Motor Skills 70(1):35–45
Alpert S, Galun M, Brandt A, Basri R (2012) Image segmentation by probabilistic bottom-up aggregation and cue integration. IEEE Trans Pattern Anal Mach Intell (TPAMI’2012) 34:315–327
Avidan S, Shamir A (2007) Seam carving for content-aware image resizing. ACM Trans Graph 26(3):10
Barghout-Stein L (1999) On differences between peripheral and foveal pattern masking. Ph.D. thesis, University of California Berkeley
Bellman R (1956) On a routing problem. Tech. rep. DTIC Document
Borji A, Itti L (2013) State-of-the-art in visual attention modeling. IEEE Trans Pattern Anal Mach Intell 35(1):185–207
Bruce N, Tsotsos J (2007) An information theoretic model of saliency and visual search. In: Attention in cognitive systems. Theories and systems from an interdisciplinary viewpoint, vol 4840. Springer, pp 171–183
Cheng M, Mitra N, Huang X, Torr P, Hu S (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell (TPAMI’2015) 37:569–582
Cline D, Hofstetter HW, Griffin JR (1997) Dictionary of visual science. Butterworth-Heinemann
Fang Y, Chen Z, Lin W, Lin CW (2012) Saliency detection in the compressed domain for adaptive image retargeting. IEEE Trans Image Process (TIP’2012) 21 (9):3888–3901
Fang Y, Lin W, Chen Z, Tsai CM, Lin CW (2014) A video saliency detection model in compressed domain. IEEE Trans Circ Syst Video Technol (TCSVT’2014) 24(1):27–38
Fu H, Chi Z, Feng D (2006) Attention-driven image interpretation with application to image retrieval. Pattern Recog 39:1604–1621
Fu H, Cao X, Tu Z (2013) Cluster-based co-saliency detection. IEEE Trans Image Process (TIP) 22(10):3766–3778
Henriques J (2010) http://www.mathworks.com/matlabcentral/fileexchange/coloredges.m
Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: IEEE Conference on computer vision and pattern recognition (CVPR’2007), pp 1–8
Itti L (2004) Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans Image Process (TIP’2004) 13:1304–1318
Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell (TPAMI’1998) 20:1254–1259
Jiang M, Xu J, Zhao Q (2014) Saliency in crowd. In: European conference on computer vision (ECCV’2014), pp 17–32
Koch C, Ullman S (1987) Shifts in selective visual attention: towards the underlying neural circuitry. In: Matters of intelligence. Springer, pp 115–141
Li Z (2014) Understanding vision: theory, models, and data. Oxford University Press
Li Z, Qin S, Itti L (2011) Visual attention guided bit allocation in video compression. Image Vis Comput 29:1–14
Liu M Y, Tuzel O, Ramalingam S, Chellappa R (2011) Entropy rate superpixel segmentation. In: IEEE Conference on computer vision and pattern recognition (CVPR’2011), pp 2097–2104
Liu T, Yuan Z, Sun J, Wang J, Zheng N, Tang X, Shum HY (2011) Learning to detect a salient object. IEEE Trans Pattern Anal Mach Intell (TPAMI’2010) 33:353–367
Ma K, Gao G, Ding G, Liu CH, Liu E (2016) Crowd saliency prediction with optimal feature combinations. In: Wireless communications & signal processing (WCSP’2016), pp 1–5
Murray N, Vanrell M, Otazu X, Parraga C (2011) Saliency estimation using a non-parametric low-level vision model. In: 2011 IEEE Conference on computer vision and pattern recognition (CVPR), pp 433–440
Panozzo D, Weber O, Sorkine O (2012) Robust image retargeting via axis-aligned deformation. Comput Graph Forum 31(2pt1):229C236
Perazzi F, Krahenbuhl P, Pritch Y, Hornung A (2012) Saliency filters: contrast based filtering for salient region detection. In: IEEE Conference on computer vision and pattern recognition (CVPR’2012), pp 733–740
Rahtu E, Kannala J, Salo M, Heikkilä J. (2010) Segmenting salient objects from images and videos. In: Computer vision-ECCV 2010. Springer, pp 366–379
Rutishauser U, Walther D, Koch C, Perona P (2004) Is bottom-up attention useful for object recognition? In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition (CVPR’2004), vol 2, pp II–37–II–44
Schmidt RF (1981) Fundamentals of sensory physiology. Springer Science & Business Media
Seo HJ, Milanfar P (2009) Static and space-time visual saliency detection by self-resemblance. J Vis 9:15
Strasburger H, Rentschler I, Jüttner M. (2011) Peripheral vision and pattern recognition: a review. J Vis 11(5):13
Toet A (2011) Computational versus psychophysical bottom-up image saliency: a comparative evaluation study. IEEE Trans Pattern Anal Mach Intell (TPAMI) 33 (11):2131–2146
Treisman AM, Gelade G (1980) A feature-integration theory of attention. Cogn Psychol 12(1):97–136
Wang L, Xue J, Zheng N, Hua G (2011) Automatic salient object extraction with contextual cue. In: IEEE International conference on computer vision (ICCV’2011), pp 105–112
Yan B, Li K, Yang X, Hu T (2015) Seam searching-based pixel fusion for image retargeting. IEEE Trans Circ Syst Vid Technol (TCSVT’2015) 25:15–23
Yang Y, Wang X, Guan T, Shen J, Yu L (2014) A multi-dimensional image quality prediction model for user-generated images in social networks. Inf Sci 281:601–610
Zhang L, Tong MH, Marks TK, Shan H, Cottrell GW (2008) Sun: a bayesian framework for saliency using natural statistics. J Vis 8(7):32
Zhaoping L, Zhaoping L (2007) Theoretical understanding of the early visual processes by data compression and data selection. Netw Comput Neural Syst 17 (4):301–34
Zhu W, Liang S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. In: 2014 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2814–2821
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This work was supported in part by NSFC (Grant No.: 61370158; 61522202).
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Tan, W., Yan, B. Salient object detection via multiple saliency weights. Multimed Tools Appl 76, 25091–25107 (2017). https://doi.org/10.1007/s11042-017-4725-7
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DOI: https://doi.org/10.1007/s11042-017-4725-7