Object-level saliency: Fusing objectness estimation and saliency detection into a uniform framework

Itti, 2007, Visual salience, Scholarpedia, 2, 3327, 10.4249/scholarpedia.3327 Li, 2014, The secrets of salient object segmentation, 280 Achanta, 2009, Frequency-tuned salient region detection, 1597 Alexe, 2012, Measuring the objectness of image windows, IEEE Trans. Pattern Anal. Mach. Intell., 34, 2189, 10.1109/TPAMI.2012.28 Carreira, 2010, Constrained parametric min-cuts for automatic object segmentation, 3241 Y.F. Ma, H.J. Zhang, Contrast-based image attention analysis by using fuzzy growing, in: Eleventh ACM International Conference on Multimedia, 2003, pp. 374–381. D.A. Klein, S. Frintrop, Center-surround divergence of feature statistics for salient object detection, in: IEEE International Conference on Computer Vision, 2012, pp. 2214–2219. Kim, 2014, Salient region detection via high-dimensional color transform, 883 Peng, 2014 Ju, 2015, Depth-aware salient object detection using anisotropic center-surround difference, Sign. Process. Image Commun., 38, 115, 10.1016/j.image.2015.07.002 J. Quo, T. Ren, J. Bei, Salient object detection for RGB-D image via saliency evolution, in: IEEE International Conference on Multimedia and Expo, 2016, pp. 1–6. Liu, 2011, Learning to detect a salient object, IEEE Trans. Pattern Anal. Mach. Intell., 33, 353, 10.1109/TPAMI.2010.70 Li, 2013, Contextual hypergraph modeling for salient object detection, 3328 Cheng, 2011, Global contrast based salient region detection, 409 Ren, 2016, How important is location information in saliency detection of natural images, Multimedia Tools Appl., 75, 2543, 10.1007/s11042-015-2875-z Yan, 2013, Hierarchical saliency detection, 1155 Y. Wei, F. Wen, W. Zhu, J. Sun, Geodesic saliency using background priors, in: Computer Vision–ECCV 2012, 2012, pp. 29–42. Yang, 2013, Saliency detection via graph-based manifold ranking, 3166 Zhu, 2014, Saliency optimization from robust background detection, 2814 Qin, 2015, Saliency detection via cellular automata, 110 J. Zhang, S. Sclaroff, Z. Lin, X. Shen, B. Price, R. Mĕch, Minimum barrier salient object detection at 80 fps, in: IEEE International Conference on Computer Vision (ICCV), 2015. Q. Wang, W. Zheng, R. Piramuthu, Grab: Visual saliency via novel graph model and background priors, in: Computer Vision and Pattern Recognition, 2016, pp. 535–543. C. Xia, J. Li, X. Chen, A. Zheng, Y. Zhang, What is and what is not a salient object? Learning salient object detector by ensembling linear exemplar regressors, in: IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4399–4407. Borji, 2012, Exploiting local and global patch rarities for saliency detection, 478 Li, 2013, Saliency detection via dense and sparse reconstruction, 2976 Li, 2015, A weighted sparse coding framework for saliency detection, 5216 Zhang, 2015, Saliency detection via sparse reconstruction and joint label inference in multiple features, Neurocomputing, 155, 1, 10.1016/j.neucom.2014.12.080 Li, 2016, Deepsaliency: multi-task deep neural network model for salient object detection, IEEE Trans. Image Process., 25, 3919, 10.1109/TIP.2016.2579306 Wang, 2017, Salient object detection: a discriminative regional feature integration approach, Int. J. Comput. Vis., 123, 251, 10.1007/s11263-016-0977-3 L. Wang, H. Lu, R. Xiang, M.H. Yang, Deep networks for saliency detection via local estimation and global search, in: IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3183–3192. R. Zhao, W. Ouyang, H. Li, X. Wang, Saliency detection by multi-context deep learning, in: Computer Vision and Pattern Recognition, 2015, pp. 1265–1274. N. Liu, J. Han, DHSnet: deep hierarchical saliency network for salient object detection, in: Computer Vision and Pattern Recognition, 2016, pp. 678–686. X. Chen, A. Zheng, J. Li, F. Lu, Look, perceive and segment: finding the salient objects in images via two-stream fixation-semantic CNNs, in: The IEEE International Conference on Computer Vision (ICCV), 2017. Uijlings, 2013, Selective search for object recognition, Int. J. Comput. Vis., 104, 154, 10.1007/s11263-013-0620-5 Cheng, 2014, Bing: binarized normed gradients for objectness estimation at 300 fps, 3286 Arbelaez, 2014, Multiscale combinatorial grouping, 328 E. Rahtu, J. Kannala, M. Blaschko, Learning a category independent object detection cascade, in: 2011 IEEE International Conference on Computer Vision (ICCV), 2011, pp. 1052–1059. Humayun, 2014, RIGOR: reusing inference in graph cuts for generating object regions, 336 Lu, 2013, Online robust dictionary learning, 415 Donoser, 2014, Discrete-continuous gradient orientation estimation for faster image segmentation, 3158 W.C. Tu, S. He, Q. Yang, S.Y. Chien, Real-time salient object detection with a minimum spanning tree, in: Computer Vision and Pattern Recognition, 2016, pp. 2334–2342. Chang, 2011, Fusing generic objectness and visual saliency for salient object detection, 914 Siva, 2013, Looking beyond the image: unsupervised learning for object saliency and detection, 3238 Feng, 2011, Salient object detection by composition, 1028 J. Zhang, S. Sclaroff, Z. Lin, X. Shen, B. Price, R. Mech, Unconstrained salient object detection via proposal subset optimization, in: Computer Vision and Pattern Recognition, 2016, pp. 5733–5742. L.B. Statistics, L. Breiman, Random forests, in: Machine Learning, 2001, pp. 5–32. Alpert, 2012, Image segmentation by probabilistic bottom-up aggregation and cue integration, IEEE Trans. Pattern Anal. Mach. Intell., 34, 315, 10.1109/TPAMI.2011.130 Arbelaez, 2009, From contours to regions: an empirical evaluation, 2294 M. Ran, L. Zelnikmanor, A. Tal, How to evaluate foreground maps, in: Computer Vision and Pattern Recognition, 2014, pp. 248–255.