Abstract
Fully convolutional networks can be used to perform end-to-end saliency detection, but pooling operations generate low-resolution results. We propose a novel recurrent architecture to generate and to refine the saliency maps. We firstly train a deep FCN (fully convolutional networks) to extract multi deep features and saliency features(denote as doublefeatures), then we feed the doublefeatures into the recurrent FCN. After each loop, the recurrent architecture generates new saliency features and saliency maps with higher resolutions and quality. We evaluate our method on many datasets, and our RDF outperforms state-of-the-art saliency detection methods.
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References
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Batra, D., Kowdle, A., Parikh, D., Luo, J., Chen, T.: iCoseg: interactive co-segmentation with intelligent scribble guidance. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010)
Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H.S., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. Pattern Analy. Mach. Intell. 37(3), 569–582 (2015)
Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Neural Information Processing Systems (2014)
Han, S.H., Jung, G.D., Lee, S.Y., Hong, Y.P., Lee, S.H.: Automatic salient object segmentation using saliency map and color segmentation. J. Cent. S. Univ. 20(9), 2407–2413 (2013)
He, S., Lau, R.W.H.: Saliency detection with flash and no-flash image pairs. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 110–124. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10578-9_8
He, S., Lau, R.W.H., Liu, W., Huang, Z., Yang, Q.: SuperCNN: a superpixelwise convolutional neural network for salient object detection. Int. J. Comput. Vis. 115(3), 330–344 (2015)
Huaizu, J., Jingdong, W., Zejian, Y., Yang, W., Nanning, Z., Shipeng, L.: Salient object detection: a discriminative regional feature integration approach. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (2013)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). arXiv:1502.03167
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)
Khuwuthyakorn, P., Robles-Kelly, A., Zhou, J.: Object of interest detection by saliency learning. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 636–649. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15552-9_46
Klein, D.A., Frintrop, S.: Center-surround divergence of feature statistics for salient object detection. In: 2011 International Conference on Computer Vision (2011)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012). http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
Li, C., Yuan, Y., Cai, W., Xia, Y., Feng, D.D.: Robust saliency detection via regularized random walks ranking. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1520–1528 (2015)
Peng, H., Li, B., Xiong, W., Hu, W., Ji, R.: RGBD salient object detection: a benchmark and algorithms. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 92–109. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10578-9_7
Pinheiro, P., Collobert, R.: Recurrent convolutional neural networks for scene labeling. In: International Conference on Machine Learning, pp. 82–90 (2014)
Rahtu, E., Kannala, J., Salo, M., Heikkilä, J.: Segmenting salient objects from images and videos. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 366–379. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15555-0_27
Sun, J., Lu, H., Li, S.: Saliency detection based on integration of boundary and soft-segmentation. In: 2012 19th IEEE International Conference on Image Processing (2012)
Tong, N., Lu, H., Zhang, Y., Ruan, X.: Salient object detection via global and local cues. Pattern Recogn. 48(10), 3258–3267 (2015)
Wei, Y., Wen, F., Zhu, W., Sun, J.: Geodesic saliency using background priors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 29–42. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33712-3_3
Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (2013)
Zhao, R., Ouyang, W., Li, H., Wang, X.: Saliency detection by multi-context deep learning. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Acknowledgments
We acknowledge the support of National Natural Science Foundation of China (No. 61231018, No. 61273366), National Science and technology support program (2015BAH31F01), Program of introducing talents of discipline to university under grant B13043.
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Wang, Z., Jiang, P., Wang, F., Zhang, X. (2016). Recurrent Double Features: Recurrent Multi-scale Deep Features and Saliency Features for Salient Object Detection. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_37
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DOI: https://doi.org/10.1007/978-3-319-48896-7_37
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