Abstract
Recent years have seen many complex models proposed for salient object detection and progressing results. However, less has been done to justify the need for such complex models as there lacks sufficient comparison to simple baselines on more challenging datasets. In this work, we propose a new baseline method for saliency detection. It simply considers a large region close to the image center as salient, and defines the saliency of a region as the product of its size and centerness. As accurate image segmentation problem is difficult by itself, we propose novel techniques that can estimate these attributes using superpixels in a soft manner, without the need to perform hard image segmentation. Our approach is based on very simple concepts and implementation, but already achieves very competitive results, especially on challenging datasets. It is further shown highly complementary with the state-of-the-art. Therefore we believe our method serves as a strong baseline and would enhance the problem understanding for future work.
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References
Borji, A., Sihite, D.N., Itti, L.: Salient object detection: a benchmark. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 414–429. Springer, Heidelberg (2012)
Liu, T., Sun, J., Tang, X., Shum, H.Y.: Learning to detect a salient object. In: CVPR (2007)
Goferman, S., Manor, L., Tal, A.: Context-aware saliency detection. In: CVPR (2010)
Cheng, M., Zhang, G., Mitra, N., Huang, X., Hu, S.: Global contrast based salient region detection. In: CVPR (2011)
Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: CVPR (2012)
Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: CVPR (2007)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: CVPR (2009)
Cheng, M.M., Warrell, J., Lin, W.Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: ICCV (2013)
Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. In: CVPR (2012)
Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR (2013)
Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: CVPR (2013)
Jiang, Z., Davis, L.S.: Submodular salient region detection. In: CVPR (2013)
Li, X., Li, Y., Shen, C., Dick, A., van den Hengel, A.: Contextual hypergraph modelling for salient object detection. In: ICCV (2013)
Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing markov chain. In: ICCV (2013)
Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: CVPR (2013)
Jiang, P., Ling, H., Yu, J., Peng, J.: Salient region detection by UFO: uniqueness, focusness and objectness. In: ICCV (2013)
Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: CVPR (2007)
Movahedi, V., Elder, J.H.: Design and perceptual validation of performance measures for salient object segmentation. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 49–56. IEEE (2010)
Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.H.: Saliency detection via dense and sparse reconstruction. In: ICCV (2013)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. PAMI 34, 2274–2281 (2012)
Fu, K., Gong, C., Gu, I., Yang, J.: Geodesic saliency propagation for image salient region detection. In: ICIP (2013)
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, Part III. LNCS, vol. 7574, pp. 29–42. Springer, Heidelberg (2012)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59, 167–181 (2004)
Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22, 888–905 (2000)
Acknowledgement
This research work was supported by the National Science Foundation of China (No.61272276, No.61305091), the National Twelfth Five-Year Plan Major Science and Technology Project of China (No.2012BAC11B01-04-03), Special Research Fund of Higher Colleges Doctorate (No.20130072110035), the Fundamental Research Funds for the Central Universities (No.2100219038), and Shanghai Pujiang Program (No.13PJ1408200).
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Zhao, L., Liang, S., Wei, Y., Jia, J. (2015). Size and Location Matter: A New Baseline for Salient Object Detection. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_38
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DOI: https://doi.org/10.1007/978-3-319-16811-1_38
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