Abstract
Salient object detection is very important in many image and vision-related applications. We add the depth clue into the detection method to extract salient objects. In low-level feature extract part, we extract the depth edge and corner clue, combining with color image features to form a 55 dimensions’ vector. In the high-level prior part, the depth prior is used to predict the probability together with the other three priors. The experiment result showed that with the depth clue, the salient detection result is improved.
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References
Treisman AM, Gelade G (1980) A feature-integration theory of attention. Cogn Psychol 12(1):97–136
Ma Y-F, Lu L, Zhang H-J, Li M (2002) A user attention model for video summarization. In: ACM Multi-media
Li L, Jiang S, Zha Z-J, Wu Z, Huang Q (2013) Partial-duplicate image retrieval via saliency-guided visual matching. IEEE MM 20(3):13–23
Avidan S, Shamir A (2007) Seam carving for content-aware image resizing. In: ACM TOG, vol 26. p 10
Navalpakkam V, Itti L (2006) An integrated model of top-down and bottom-up attention for optimizing detection speed. In: CVPR. pp 2049–2056
Borji A (2012) Boosting bottom-up and top-down visual features for saliency estimation[C]. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 438–445
Peng H, Li B, Xiong W, Hu W, Ji R (2014) Rgbd salient object detection: a benchmark and algorithms. In: European conference on computer vision (ECCV). pp 92–109
Ju R, Liu Y, Ren T et al (2015) Depth-aware salient object detection using anisotropic center-surround difference[J]. Sig Process Image Commun 38:115–126
Desingh K, Madhava Krishna K, Rajan D et al (2013) Depth really matters: improving visual salient region detection with depth[C]. BMVC
Lang C, Nguyen T V, Katti H et al (2012) Depth matters: Influence of depth cues on visual saliency. Computer Vision–ECCV 2012. Springer, Berlin, pp 101–115
Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Su ̈sstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI 34(11):2274–2282
Peng H, Li B, Ling H, Hu W, Xiong W, Maybank SJ (1986) Salient object detection via structured matrix decomposition. Canny J. A computational approach to edge detection[J]. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, no 6. pp 679–698
Harris C, Stephens MA (1988) Combined corner and edge detector[C]. Alvey Vis Conf 15:50
Simoncelli EP, Freeman WT (1995) The steerable pyramid: a flexible architecture for multi-scale derivative computation. In: ICIP
Feichtinger HG, Strohmer T (1998) Gabor analysis and al-gorithms: theory and applications
Zhu W, Liang S, Wei Y et al (2014) Saliency optimization from robust background detection[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2814–2821
Shen X Wu Y (2012) A unified approach to salient object detection via low rank matrix recovery. In: CVPR. pp 2296–2303
Judd T, Ehinger K, Durand F, Torralba A (2009) Learning to predict where humans look. In: ICCV
Cheng MM, Warrell J, Lin WY, Zheng S, Vineet V, Crook N: Efficient salient region detection with soft image abstraction. In: ICCV
Goferman S, Manor LZ, Tal A (2010) Context-aware saliency detection. In: CVPR
Yan Q, Xu L, Shi J, Jia J (2013) Hierarchical saliency detection. In: CVPR
Acknowledgments
This work was supported by Beijing Natural Science Foundation (No. 4162019) and General Project of Beijing Municipal Education Commission Science and Technology Development Plans (No. SQKM201610011010).
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Cai, Q., Wei, L., Li, H., Cao, J. (2016). Salient Object Detection Based on RGBD Images. In: Jia, Y., Du, J., Zhang, W., Li, H. (eds) Proceedings of 2016 Chinese Intelligent Systems Conference. CISC 2016. Lecture Notes in Electrical Engineering, vol 405. Springer, Singapore. https://doi.org/10.1007/978-981-10-2335-4_40
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DOI: https://doi.org/10.1007/978-981-10-2335-4_40
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