Abstract
Salient object detection is very useful in many computer vision applications such as image segmentation, content-based image editing and object recognition. In this paper, we present a salient object detection algorithm by using color spatial distribution (CSD) and minimum spanning tree weight (MSTW). We first use a segmentation algorithm to decompose an image into superpixel-level elements, then use these elements as nodes to construct a minimum spanning tree (MST), each connected edge weight is the mean color difference between two nodes. CSD of each element can be computed by integrating color, spatial distance and MSTW. Note that if the color of one element is the most widely distributed over the entire image, it should have the biggest CSD value, we regard this element as a background node (BG Node). Then we use the MSTW between other element and BG node to generate a MSTW map. The superpixel-level saliency map can be obtained by combining the CSD map and MSTW map. Finally, we use a guided filter to get the pixel-level saliency map. Experimental results on two databases demonstrate that our proposed method outperforms other previous state-of-the-art approaches.
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Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: CVPR, pp 1597–1604
Chen T, Tan P, Ma L, Cheng M, Shamir A, Hu S Poseshop: human image database construction and personalized content synthesis. IEEE Trans Vis Comput Graph 19(5):824–837
Cheng M, Zhang G, Mitra N, Huang X, Hu S (2011) Global contrast based salient region detection. In: CVPR, pp 409–416
Cheng MM, Warrell J, Lin WY, Zheng S, Vineet V, Crook N (2013) Efficient salient region detection with soft image abstraction. In: IEEE ICCV, pp 1529–1536
Erdem E, Erdem A (2013) Visual saliency estimation by nonlinearly integrating features using region covariances. J Vis 13(4):11
Foncubierta-Rodríguez A, Müller H, Depeursinge A (2013) Region-based volumetric medical image retrieval. In: SPIE medical imaging. international society for optics and photonics
Gao Y, Tang J, Hong R, Yan S, Dai Q, Zhang N, Chua TS (2012) Camera constraint-free view-based 3-d object retrieval. IEEE Trans Image Process 21(4):2269–2281
Goferman S, Zelnik-Manor L, Tal A (2012) Context-aware saliency detection. IEEE Trans Pattern Anal Mach Intell 34(10):1915–1926
Han J, Ngan KN, Li M, Zhang HJ (2006) Unsupervised extraction of visual attention objects in color images. IEEE Trans Circ Syst Video Technol 16(1):141–145
Harel J, Koch C, Perona P (2007) Graph-based visual saliency. In: Neural information processing systems, pp 545–552
He K, Sun J, Tang X (2010) Guided image filtering. In: ECCV, pp 1–14
Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: CVPR, pp 1–8
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
Jing H, Han Q, He X, Niu X (2013) Background contrast based salient region detection. Neurocomputing 124:57–62
Kim J, Han D, Tai YW, Kim J (2014) Salient region detection via high-dimensional color transform. In: 2014 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 883–890
Li X, Li Y, Shen C, Dick A, Hengel Avd (2013) Contextual hypergraph modelling for salient object detection. In: ICCV
Liang Z, Wang M, Zhou X, Lin L, Li W (2014) Salient object detection based on regions. Multimed Tools Appl 68(3):517–544
Liu Q, Han T, Sun Y, Chu Z, Shen B (2013) A two step salient objects extraction framework based on image segmentation and saliency detection. Multimed Tools Appl 67(1):231–247
Liu S, He D, Liang X (2012) An improved hybrid model for automatic salient region detection. IEEE Signal Process Lett 19(4):207–210
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 33(2):353–367
Liu Y, Li X, Wang L, Niu Y, Liu F (2012) Oscillation analysis for salient object detection. Multimed Tools Appl:1–21
Liu Z, Le Meur O, Luo S, Shen L (2013) Saliency detection using regional histograms. Opt Lett 38(5):700–702
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Margolin R, Tal A, Zelnik-Manor L (2013) What makes a patch distinct? In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1139–1146
Movahedi V, Elder JH (2010) Design and perceptual validation of performance measures for salient object segmentation. In: 2010 IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW). IEEE, pp 49–56
Navalpakkam V, Itti L (2006) An integrated model of top-down and bottom-up attention for optimizing detection speed. In: CVPR, pp 2049–2056
Perazzi F, Krähenbühl P, Pritch Y, Hornung A (2012) Saliency filters: contrast based filtering for salient region detection. In: CVPR, pp 733–740
Rutishauser U, Walther D, Koch C, Perona P (2004) Is bottom-up attention useful for object recognition? In: CVPR, pp 37–44
Shen X, Wu Y (2012) A unified approach to salient object detection via low rank matrix recovery. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 853–860
Thanh Nguyen D, Ogunbona PO, Li W (2013) A novel shape-based non-redundant local binary pattern descriptor for object detection. Pattern Recog 46(5):1485–1500
Tong N, Lu H, Zhang L, Ruan X (2014) Saliency detection with multi-scale superpixels. IEEE Signal Process Lett 21(9):1035–1039
Veksler O, Boykov Y, Mehrani P (2010) Superpixels and supervoxels in an energy optimization framework. In: ECCV, pp 211–224
Wei Y, Wen F, Zhu W, Sun J (2012) Geodesic saliency using background priors. In: ECCV, pp 29–42
Wu H, Wang YS, Feng KC, Wong TT, Lee TY, Heng PA (2010) Resizing by symmetry-summarization. ACM Trans Graph (TOG) 29(6):159
Yang Q (2014) Stereo matching using tree filtering. IEEE Trans Pattern Anal Mach Intell. doi:10.1109/TPAMI.2014.2353642
Yu JG, Tian J (2012) Saliency detection using midlevel visual cues. Optics Lett 37(23):4994–4996
Zhu W, Liang S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2814–2821
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This work is funded by the 863 Program of China (2012AA03A301), NSFC (61201179 and 91320201), Ph.D. Pro-grams Foundation of the Ministry of Education of China (20130032110010).
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Tang, C., Hou, C., Wang, P. et al. Salient object detection using color spatial distribution and minimum spanning tree weight. Multimed Tools Appl 75, 6963–6978 (2016). https://doi.org/10.1007/s11042-015-2622-5
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DOI: https://doi.org/10.1007/s11042-015-2622-5