Detection of Co-salient Objects by Looking Deep and Wide (original) (raw)

References

• Achanta, R., Hemami, S., Estrada, F., & Susstrunk, S. (2009). Frequency-tuned salient region detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1597–1604).
• Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Susstrunk, S. (2012). Slic superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274–2282.
  Article Google Scholar
• Akamine, K., Fukuchi, K., Kimura, A., & Takagi, S. (2012). Fully automatic extraction of salient objects from videos in near real time. The Computer Journal, 55(1), 3–14.
  Article Google Scholar
• Batra, D., Kowdle, A., Parikh, D., Luo, J., & Chen, T. (2010). icoseg: Interactive co-segmentation with intelligent scribble guidance. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3169–3176).
• Bengio, Y. (2009). Learning deep architectures for ai. Foundations and trends in Machine Learning, 2(1), 1–127.
  Article MathSciNet MATH Google Scholar
• Boiman, O., Shechtman, E., & Irani, M. (2008) In defense of nearest-neighbor based image classification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–8).
• Cao, X., Cheng, Y., Tao, Z., & Fu, H. (2014). Co-saliency detection via base reconstruction. In Proceedings of the ACM international conference on multimedia (pp. 997–1000).
• Cao, X., Tao, Z., Zhang, B., Fu, H., & Feng, W. (2014). Self-adaptively weighted co-saliency detection via rank constraint. IEEE Transactions on Image Processing, 22(9), 4175–4182.
  MathSciNet Google Scholar
• Chen, H.-T. (2010). Preattentive co-saliency detection. In Proceedings of the IEEE international conference on image processing (pp. 1117–1120).
• Chen, X., Shrivastava, A., & Gupta, A. (2014). Enriching visual knowledge bases via object discovery and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2035–2042).
• Cheng, M.-M., Zhang, Z., Lin, W.-Y. & Torr, P. (2014). Bing: Binarized normed gradients for objectness estimation at 300fps. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3286–3293).
• Dai, J., Wu, Y. N., Zhou, J. & Zhu,S.-C. (2013). Cosegmentation and cosketch by unsupervised learning. In Proceedings of the IEEE international conference on computer vision (pp. 1305–1312).
• Eichner, M., & Ferrari, V. (2012). Human pose co-estimation and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2282–2288.
  Article Google Scholar
• Fu, H., Cao, X., & Tu, Z. (2013). Cluster-based co-saliency detection. IEEE Transactions on Image Processing, 22(10), 3766–3778.
  Article MathSciNet Google Scholar
• Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580–587).
• Goferman, S., Zelnik-Manor, L., & Tal, A. (2012). Context-aware saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(10), 1915–1926.
  Article Google Scholar
• Guo, J., Li, Z., Cheong, L.-F., & Zhou, S. Z. (2013). Video co-segmentation for meaningful action extraction. In Proceedings of the IEEE international conference on computer vision (pp. 2232–2239).
• Jacobs, D. E., Goldman, D. B., & Shechtman, E. (2010). Cosaliency: Where people look when comparing images. In Proceedings of the 23nd annual ACM symposium on User interface software and technology (pp. 219–228).
• Jia, Y., & Han, M. (2013). Category-independent object-level saliency detection. In Proceedings of the IEEE international conference on computer vision (pp. 1761–1768).
• Jiang, H., Wang, J., Yuan, Z., Liu, T., & Zheng, N. (2011). Automatic salient object segmentation based on context and shape prior. In Proceedings of the British machine vision conference (pp. 1–12).
• Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., & Li, S. (2013). Salient object detection: A discriminative regional feature integration approach. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2083–2090).
• Joulin, A., Bach, F., & Ponce, J. (2010). Discriminative clustering for image co-segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1943–1950).
• Joulin, A., Bach, F., & Ponce, J. (2012). Multi-class cosegmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 542–549).
• Kim, G., Xing, E. P., Fei-Fei, L., & Kanade, T. (2011). Distributed cosegmentation via submodular optimization on anisotropic diffusion. In Proceedings of the IEEE conference on computer vision (pp. 169–176).
• Kuettel, D., & Ferrari, V. (2012). Figure-ground segmentation by transferring window masks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 558–565).
• Lee, W.-F., Huang, T.-H., Yeh, S.-L., & Chen, H. H. (2011). Learning-based prediction of visual attention for video signals. IEEE Transactions on Image Processing, 20(11), 3028–3038.
  Article MathSciNet Google Scholar
• Li, W.-T., Chang, H.-S., Lien, K.-C., Chang, H.-T., & Wang, Y. (2013). Exploring visual and motion saliency for automatic video object extraction. IEEE Transactions on Image Processing, 22(7), 2600–2610.
  Article Google Scholar
• Li, Y., Fu, K., Liu, Z., & Yang, J. (2015). Efficient saliency-model-guided visual co-saliency detection. IEEE Signal Processing Letters, 22(5), 588–592.
  Article Google Scholar
• Li, H., Meng, F., & Ngan, K. N. (2013). Co-salient object detection from multiple images. IEEE Transactions on Multimedia, 15(8), 1896–1909.
  Article Google Scholar
• Li, H., & Ngan, K. N. (2011). A co-saliency model of image pairs. IEEE Transactions on Image Processing, 20(12), 3365–3375.
  Article MathSciNet Google Scholar
• Li, Y., Sheng, B., Ma, L., Wu, W., & Xie, Z. (2013). Temporally coherent video saliency using regional dynamic contrast. IEEE Transactions on Circuits and Systems for Video Technology, 23(12), 2067–2076.
• Li, J., Tian, Y., & Huang, T. (2014). Visual saliency with statistical priors. International Journal of Computer Vision, 107(3), 239–253.
  Article MathSciNet MATH Google Scholar
• Li, J., Tian, Y., Huang, T., & Gao, W. (2010). Probabilistic multi-task learning for visual saliency estimation in video. International Journal of Computer Vision, 90(2), 150–165.
  Article Google Scholar
• Liu, Z., Zou, W., Li, L., Shen, L., & Le Meur, O. (2014). Co-saliency detection based on hierarchical segmentation. IEEE Signal Processing Letters, 21(1), 88–92.
  Article Google Scholar
• Marat, S., Phuoc, T. H., Granjon, L., Guyader, N., Pellerin, D., & Guérin-Dugué, A. (2009). Modelling spatio-temporal saliency to predict gaze direction for short videos. International Journal of Computer Vision, 82(3), 231–243.
  Article Google Scholar
• Meng, F., Li, H., Liu, G., & Ngan, K. N. (2012). Object co-segmentation based on shortest path algorithm and saliency model. IEEE Transactions on Multimedia, 14(5), 1429–1441.
  Article Google Scholar
• Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014). Learning and transferring mid-level image representations using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1717–1724).
• Prest, A., Leistner, C., Civera, J., Schmid, C., & Ferrari, V. (2012). Learning object class detectors from weakly annotated video. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3282–3289).
• Rubinstein, M., Joulin, A., Kopf, J. & Liu, C. (2013). Unsupervised joint object discovery and segmentation in internet images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1939–1946).
• Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211–252.
  Article MathSciNet Google Scholar
• Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229.
• Shen, X., & Wu, Y. (2012). A unified approach to salient object detection via low rank matrix recovery. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 853–860).
• Siva, P., Russell, C., & Xiang,T. (2012). In defence of negative mining for annotating weakly labelled data. In Proceedings of the European conference on computer vision (pp. 594–608).
• Siva, P., Russell, C., Xiang, T., & Agapito,L. (2013). Looking beyond the image: Unsupervised learning for object saliency and detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3238–3245).
• Tan, Z., Wan, L., Feng, W., & Pun, C.-M. (2013). Image co-saliency detection by propagating superpixel affinities. In Proceedings of the IEEE international conference on acoustics, speech and signal processing (pp. 2114–2118).
• Tang, K., Joulin, A., Li, L.-J., & Fei-Fei, L. (2014). Co-localization in real-world images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1464–1471).
• Tian, Y., Li, J., Yu, S., & Huang, T. (2014). Learning complementary saliency priors for foreground object segmentation in complex scenes. International Journal of Computer Vision, 111(2), 153–170.
  Article Google Scholar
• Tighe, J., & Lazebnik, S. (2013). Finding things: Image parsing with regions and per-exemplar detectors. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3001–3008).
• Toshev, A., Shi, J., & Daniilidis, K. (2007). Image matching via saliency region correspondences. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–8).
• Vicente, S., Rother, C., & Kolmogorov, V. (2011). Object cosegmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2217–2224).
• Wang Z., & Liu, R. (2012). Semi-supervised learning for large scale image cosegmentation. In Proceedings of the IEEE international conference on computer vision (pp. 393–400).
• Wang, L., Hua, G., Sukthankar, R., Xue, J., & Zheng, N. (2014). Video object discovery and co-segmentation with extremely weak supervision. In Proceedings of the European conference on computer vision (pp. 640–655).
• Wang, J., DaSilva, M. P., LeCallet, P., & Ricordel, V. (2013). Computational model of stereoscopic 3d visual saliency. IEEE Transactions on Image Processing, 22(6), 2151–2165.
• Winn, J., Criminisi, A., & Minka, T. (2005). Object categorization by learned universal visual dictionary. In Proceedings of the IEEE international conference on computer vision (pp. 1800–1807).
• Xie, Y., Lu, H., & Yang, M.-H. (2013). Bayesian saliency via low and mid level cues. IEEE Transactions on Image Processing, 22(5), 1689–1698.
  Article MathSciNet Google Scholar
• Xue, J., Wang, L., Zheng, N., & Hua, G. (2013). Automatic salient object extraction with contextual cue and its applications to recognition and alpha matting. Pattern Recognition, 46(11), 2874–2889.
  Article Google Scholar
• Yang, C., Zhang, L., Lu, H., Ruan, X., & Yang, M.-H. (2013). Saliency detection via graph-based manifold ranking. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3166–3173).
• Zhang, D., Han, J., Li, C., & Wang, J. (2015). Co-saliency detection via looking deep and wide. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2994–3002).
• Zhang, L., Tong, M. H., Marks, T. K., Shan, H., & Cottrell, G. W. (2008). Sun: A bayesian framework for saliency using natural statistics. Journal of Vision, 8(7), 32.
  Article Google Scholar
• Zhou, D., Weston, J., Gretton, A., Bousquet, O., & Schölkopf, B. (2004). Ranking on data manifolds. In Proceedings of advances in neural information processing systems (pp. 169–176).
• Zhu, J.-Y., Wu, J. Wei, Y., Chang, E., & Tu, Z. (2012). Unsupervised object class discovery via saliency-guided multiple class learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3218–3225).

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