SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

International Journal of Computer Vision

Volumn 115, Issue 3, 2015, Pages 330-344

  He, Shengfeng ^a   Lau, Rynson W H ^a   Liu, Wenxi ^a   Huang, Zhe ^a   Yang, Qingxiong ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Convolutional neural networks; Deep learning; Feature learning; Saliency detection

Indexed keywords

COMPUTER VISION; CONVOLUTION; FEATURE EXTRACTION; NEURAL NETWORKS; OBJECT RECOGNITION;

CONTEXTUAL INFORMATION; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; FEATURE LEARNING; INTERNAL REPRESENTATION; SALIENCY DETECTION; SALIENT OBJECT DETECTION; STATE-OF-THE-ART METHODS;

OBJECT DETECTION;

EID: 84947025839     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-015-0822-0     Document Type: Article

References (50)

1. Achanta, R., Hemami, S., Estrada, F., & Susstrunk, S. (2009). Frequency-tuned salient region detection. In CVPR (pp. 1597–1604).
2. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Ssstrunk, S. (2012). SLIC superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI (pp. 2274–2282).
3. Arbelaez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour detection and hierarchical image segmentation. IEEE TPAMI, 33(5), 898–916.
4. Avidan, S., & Shamir, A. (2007). Seam carving for content-aware image resizing. ACM TOG, 26(3), 10.
5. Bell, R., & Koren, Y. (2007). Lessons from the netflix prize challenge. SIGKDD Explorations Newsletter, 9(2), 75–79.
6. Borji, A. (2012). Boosting bottom-up and top-down visual features for saliency estimation. In CVPR (pp. 438–445).
7. Borji, A., Sihite, D., & Itti, L. (2012). Salient object detection: A benchmark. In ECCV.
8. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
9. Cheng, M., Zhang, G., Mitra, N., Huang, X., & Hu, S. (2011). Global contrast based salient region detection. In CVPR (pp. 409–416).
10. Ciresan, D., Meier, U., Masci, J., & Schmidhuber, J. (2011). A committee of neural networks for traffic sign classification. In IJCNN (pp. 1918–1921).
11. Ciresan, D.C., Meier, U., & Schmidhuber, J. (2012). Multi-column deep neural networks for image classification. In CVPR (pp. 3642–3649).
12. Collobert, R., Kavukcuoglu, K., & Farabet, C. (2011). Torch7: A Matlab-like environment for machine learning. In BigLearn NIPS Workshop.
13. Einhauser, W., & Konig, P. (2003). Does luminance-contrast contribute to a saliency map for overt visualn attention? European Journal of Neuroscience, 17(5), 1089–1097.
14. Farabet, C., Couprie, C., Najman, L., & Lecun, Y. (2013). Learning hierarchical features for scene labeling. IEEE TPAMI, 35(8), 1915–1929.
15. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR.
16. Goferman, S., Zelnik-Manor, L., & Tal, A. (2010). Context-aware saliency detection. In CVPR.
17. Harel, J., Koch, C., & Perona, P. (2007). Graph-based visual saliency. In NIPS (pp. 545–552).
18. He, S., & Lau, R. (2014). Saliency detection with flash and no-flash image pairs. In ECCV (pp. 110–124).
19. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. CoRR abs/1207.0580.
20. Intriligator, J., & Cavanagh, P. (2001). The spatial resolution of visual attention. Cognitive Psychology, 43(3), 171–216.
21. Itti, L., & Koch, C. (2001). Computational modelling of visual attention. Nature Reviews Neuroscience, 2(3), 194–203.
22. Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE TPAMI, 20(11), 1254–1259.
23. Jiang, H., Wang, J., Yuan, Z., Liu, T., & Zheng, N. (2011). Automatic salient object segmentation based on context and shape prior. In BMVC.
24. Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., & Li, S. (2013). Salient object detection: A discriminative regional feature integration approach. In CVPR (pp. 2083–2090).
25. Jiang, P., Ling, H., Yu, J., & Peng, J. (2013). Salient region detection by ufo: Uniqueness, focusness and objectness. In ICCV.
26. Koch, C., & Ullman, S. (1985). Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology, 4, 219–227.
27. Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). Imagenet classification with deep convolutional neural networks. In NIPS (pp. 1106–1114).
28. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
29. Lee, T., & Mumford, D. (2003). Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America A, 20(7), 1434–1448.
30. Li, S., Liu, Z.Q., & Chan, A. (2014). Heterogeneous multi-task learning for human pose estimation with deep convolutional neural network. In IJCV (pp. 1–18).
31. Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., et al. (2011). Learning to detect a salient object. IEEE TPAMI, 33(2), 353–367.
32. Lu, Y., Zhang, W., Jin, C., & Xue, X. (2012). Learning attention map from images. In CVPR (pp. 1067–1074).
33. Ma, Y., & Zhang, H. (2003). Contrast-based image attention analysis by using fuzzy growing. In ACM Multimedia (pp. 374–381).
34. Macaluso, E., Frith, C., & Driver, J. (2002). Directing attention to locations and to sensory modalities: Multiple levels of selective processing revealed with pet. Cerebral Cortex, 12(4), 357–368.
35. Marchesotti, L., Cifarelli, C., & Csurka, G. (2009). A framework for visual saliency detection with applications to image thumbnailing. In CVPR (pp. 2232–2239).
36. Margolin, R., Tal, A., & Zelnik-Manor, L. (2013). What makes a patch distinct? In CVPR.
37. Ming-Chng, Warrell, J., Lin, W., Zheng, S., Vineet, V., & Crook, N. (2013). Efficient salient region detection with soft image abstraction. In ICCV.
38. Moore, A., Prince, S., Warrell, J., Mohammed, U., & Jones, G. (2008). Superpixel lattices. In CVPR (pp. 1–8).
39. Osadchy, M., LeCun, Y., & Miller, M. (2007). Synergistic face detection and pose estimation with energy-based models. Journal of Machine Learning Research, 8, 1197–1215.
40. Parkhurst, D., Law, K., & Niebur, E. (2002). Modeling the role of salience in the allocation of overt visual attention. Vision Research, 42(1), 107–123.
41. Perazzi, F., Krähenbühl, P., Pritch, Y., & Hornung, A. (2012). Saliency filters: Contrast based filtering for salient region detection. In CVPR (pp. 733–740).
42. Pinheiro, P., & Collobert, R. (2014). Recurrent convolutional neural networks for scene parsing. In ICML (pp. 82–90).
43. Shen, C., Mingli, S., & Zhao, Q. (2012). Learning high-level concepts by training a deep network on eye fixations. In Deep Learning and Unsupervised Feature Learning NIPS Workshop.
44. Sun, Y., Wang, X., & Tang, X. (2013). Deep convolutional network cascade for facial point detection. In CVPR (pp. 3476–3483).
45. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014). Going deeper with convolutions. CoRR abs/1409.4842.
46. Tatler, B. (2007). The central fixation bias in scene viewing: Selecting an optimal viewing position independently of motor biases and image feature distributions. Journal of Vision, 7(14), 4, 1–17.
47. Toet, A. (2011). Computational versus psychophysical bottom-up image saliency: A comparative evaluation study. IEEE TPAMI, 33(11), 2131–2146.
48. Winnemoller, H., Kyprianidis, J., & Olsen, S. (2012). Xdog: An extended difference-of-gaussians compendium including advanced image stylization. Computers & Graphics, 36(6), 740–753.
49. Yan, Q., Xu, L., Shi, J., & Jia, J. (2013). Hierachical saliency detection. In CVPR.
50. Yang, C., Zhang, L., Lu, H., Ruan, X., & Yang, M. (2013). Saliency detection via graph-based manifold ranking. In CVPR.