Abstract
In this paper, we describe an approach to saliency detection as a two-category (salient or not) soft clustering using topic model. In order to simulate human’s paralleled visual neural perception, many sub-regions are sampling from an image, where each one is considered as a set of colors from a codebook, which is a color palette for the image. We assume salient pixels would appear spatial adjacent more possibly, therefore in a same sub-region, while less salient pixels would either. Consequently, all the sub-regions are clustered into two assumed topics with probabilities: “salient”/“non-salient”, while “salient” one is decided to give saliency value of each pixel according to its posterior conditional probability. Our method will give a global saliency map with full resolution, and experiments illustrate it is competitive with the state-of-art methods.
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References
Rutishauser, U., Walther, D., Koch, C., Perona, P.: Is bottom-up attention useful for object recognition? In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, p. II–37 (2004)
Chen, T., Cheng, M., Tan, P., Shamir, A., Hu, S.: Sketch2photo: internet image montage. ACM Transactions on Graphics (TOG) 28, 124 (2009)
Zhang, G., Yuan, Z., Zheng, N., Sheng, X., Liu, T.: Visual Saliency Based Object Tracking. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009, Part II. LNCS, vol. 5995, pp. 193–203. Springer, Heidelberg (2010)
Leavers, V.: Preattentive computer vision towards a two-stage computer vision system for the extraction of qualitative descriptors and the cues for focus of attention. Image and Vision Computing 12(9), 583–599 (1994)
van der Heijden, A.: Two stages in visual information processing and visual perception? Visual Cognition 3(4), 325–362 (1996)
Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)
Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 1–8 (2008)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of the Eleventh ACM International Conference on Multimedia (MM), pp. 374–381. ACM, New York (2003)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. Advances in Neural Information Processing Systems 19, 545 (2007)
Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(2), 353–367 (2011)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1597–1604 (2009)
Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th Annual ACM International Conference on Multimedia (MM), pp. 815–824 (2006)
Cheng, M., Zhang, G., Mitra, N., Huang, X., Hu, S.: Global contrast based salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 409–416 (2011)
Li, Y., Zhou, Y., Xu, L., Yang, X., Yang, J.: Incremental sparse saliency detection. In: IEEE International Conference on Image Processing (ICIP), pp. 3093–3096 (2009)
Han, B., Zhu, H., Ding, Y.: Bottom-up saliency based on weighted sparse coding residual. In: Proceedings of the 19th ACM International Conference on Multimedia (MM), pp. 1117–1120 (2011)
Duan, L., Wu, C., Miao, J., Qing, L., Fu, Y.: Visual saliency detection by spatially weighted dissimilarity. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 473–480 (2011)
Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering objects and their location in images. In: IEEE International Conference on Computer Vision, vol. 1, pp. 370–377 (2005)
Li, Z., Wang, Y., Chen, J., Xu, J., Larid, J.: Image topic discovery with saliency detection. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 33–31 (2010)
Li, Z., Xu, J., Wang, Y., Geers, G., Yang, J.: Saliency detection based on proto-objects and topic model. In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 125–131 (2011)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42(1), 177–196 (2001)
Tatler, B.: 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)
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Jiang, G., Liu, X., Yue, J., Shi, Z. (2013). Exploit Spatial Relationships among Pixels for Saliency Region Detection Using Topic Model. In: Li, S., et al. Advances in Multimedia Modeling. MMM 2013. Lecture Notes in Computer Science, vol 7732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35725-1_16
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DOI: https://doi.org/10.1007/978-3-642-35725-1_16
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