cvpr cvpr2013 cvpr2013-202 cvpr2013-202-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Qiong Yan, Li Xu, Jianping Shi, Jiaya Jia
Abstract: When dealing with objects with complex structures, saliency detection confronts a critical problem namely that detection accuracy could be adversely affected if salient foreground or background in an image contains small-scale high-contrast patterns. This issue is common in natural images and forms a fundamental challenge for prior methods. We tackle it from a scale point of view and propose a multi-layer approach to analyze saliency cues. The final saliency map is produced in a hierarchical model. Different from varying patch sizes or downsizing images, our scale-based region handling is by finding saliency values optimally in a tree model. Our approach improves saliency detection on many images that cannot be handled well traditionally. A new dataset is also constructed. –
[1] R. Achanta, F. J. Estrada, P. Wils, and S. S ¨usstrunk. Salient region detection and segmentation. In ICVS, pages 66–75, 2008.
[2] R. Achanta, S. S. Hemami, F. J. Estrada, and S. S ¨usstrunk. Frequency-tuned salient region detection. In CVPR, pages 1597–1604, 2009.
[3] K.-Y. Chang, T.-L. Liu, and S.-H. Lai. From co-saliency to co-segmentation: An efficient and fully unsupervised energy minimization model. In CVPR, pages 2129–2136, 2011.
[4] M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, and S.M. Hu. Global contrast based salient region detection. In CVPR, pages 409–416, 2011.
[5] D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 24(5):603–619, 2002.
[6] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascalnetwork.org/challenges/VOC/voc2012/workshop/index.html.
[7] P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graphbased image segmentation. International Journal of Computer Vision, 59(2): 167–181, 2004.
[8] S. Goferman, L. Zelnik-Manor, and A. Tal. Context-aware saliency detection. In CVPR, pages 2376–2383, 2010.
[9] R. Gonzalez and R. Woods. Digital image processing. Prentice Hall Press, ISBN 0-201-18075-8, 2002.
[10] J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. In NIPS, pages 545–552, 2006.
[11] P. Hiremath and J. Pujari. Content based image retrieval us-
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21] ing color boosted salient points and shape features of an image. International Journal of Image Processing, 2(1): 10–17, 2008. X. Hou and L. Zhang. Saliency detection: A spectral residual approach. In CVPR, 2007. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell., 20(1 1): 1254–1259, 1998. T. Judd, K. A. Ehinger, F. Durand, and A. Torralba. Learning to predict where humans look. In ICCV, pages 2106–21 13, 2009. D. A. Klein and S. Frintrop. Center-surround divergence of feature statistics for salient object detection. In ICCV, 2011. F. R. Kschischang, B. J. Frey, and H.-A. Loeliger. Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory, 47(2):498–519, 2001. T. Lee and D. Mumford. Hierarchical bayesian inference in the visual cortex. JOSA A, 20(7): 1434–1448, 2003. T. Liu, J. Sun, N. Zheng, X. Tang, and H.-Y. Shum. Learning to detect a salient object. In CVPR, 2007. Y.-F. Ma and H. Zhang. Contrast-based image attention analysis by using fuzzy growing. In ACM Multimedia, pages 374–381, 2003. E. Macaluso, C. Frith, and J. Driver. Directing attention to locations and to sensory modalities: Multiple levels of selective processing revealed with pet. Cerebral Cortex, 12(4):357–368, 2002. D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, volume 2, pages 416–423, July 2001 .
[22] S. Palmer. Vision science: Photons to phenomenology, volume 1. MIT press Cambridge, MA, 1999.
[23] F. Perazzi, P. Krahenbuhl, Y. Pritch, and A. Hornung. Saliency filters: Contrast based filtering for salient region detection. In CVPR, 2012.
[24] G. Sharma, F. Jurie, and C. Schmid. Discriminative spatial saliency for image classification. In CVPR, pages 3506– 3513, 2012.
[25] X. Shen and Y. Wu. A unified approach to salient object detection via low rank matrix recovery. In CVPR, 2012.
[26] B. Tatler. 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), 2007.
[27] A. Toet. Computational versus psychophysical bottom-up image saliency: A comparative evaluation study. IEEE Trans. Pattern Anal. Mach. Intell., 33(1 1):213 1–2146, 2011.
[28] A. M. Treisman and G. Gelade. A feature-integration theory of attention. Cognitive Psychology, 12(1):97–136, Jan. 1980.
[29] D. Walther, L. Itti, M. Riesenhuber, T. Poggio, and C. Koch. Attentional selection for object recognitionła gentle way. In Biologically Motivated Computer Vision, pages 251–267. Springer, 2002.
[30] Y. Wei, F. Wen, W. Zhu, and J. Sun. Geodesic saliency using background priors. In ECCV, 2012. [3 1] Y. Zhai and M. Shah. Visual attention detection in video sequences using spatiotemporal cues. In ACM Multimedia, pages 815–824, 2006. 1 1 1 111116666622000