Abstract
In this paper, we propose a novel feature correlation and fusion approach for multiple visual focuses content associational problem. Integrating various visual attention models to extract the visual focus of the image in the visual database, a weighted fusion of visual focuses will be obtained in good accuracy and the corresponding visual focus set will also be built subsequently. Then, the correlation matrix based on normalized mutual information and structural similarity index measurement will be computed within visual focus set. Through scanning correlation matrix in turn, the corresponding focus fusion process will be carried on and we use the weighted saliency model to compute visual focus of fusion focus. Compared with the state-of-the-art methods such as Itti, IS, GBVS, IF, NCS, MS and ISRW, higher robustness and accuracy rate are the main outstanding advantages of the presented approach. Experimental results on high noise interference confirm the validity of our approach.
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References
Lee, S.-H., Choi, S.-B., Lee, M., et al.: Non-uniform image compression using a biologically motivated selective attention model. Neurocomputing 67, 350–356 (2005)
Gupta, R., Khanna, M.T., Chaudhury, S.: Visual saliency guided video compression algorithm. Signal Process. Image Commun. 28, 1006–1022 (2013)
Jiang, Q., Shao, F., Jiang, G., et al.: A depth perception and visual comfort guided computational model for stereoscopic 3D visual saliency. Signal Process. Image Commun. 38, 57–69 (2015)
Ji, Q.-G., Fang, Z.-D., Xie, Z.-H., et al.: Video abstraction based on the visual attention model and online clustering. Signal Process. Image Commun. 28, 241–253 (2013)
Wang, X.-Y., Yong-Jian, Yu., Yang, H.-Y.: An effective image retrieval scheme using color, texture and shape features. Comput. Stan. Interfaces 33, 59–68 (2011)
Borji, A., Itti, L.: State-of-the-art in visual attention modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 185–207 (2013)
Hou, X., Harel, J., Koch, C.: Image signature: high lighting sparse salient regions. IEEE Trans. Pattern Anal. Mach. Intell. 34, 194–201 (2012)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, Proceedings of the 2006 Conference, vol. 19, pp. 545–552 (2007)
Xia, C., FeiQi, G.S., et al.: Nonlocal center–surround reconstruction-based bottom-up saliency estimation. Pattern Recognit. 48, 1337–1348 (2015)
Jing, H., He, X., Han, Q., et al.: Saliency detection based on integrated features. Neurocomputing 129, 114–121 (2014)
Li, J., Fang, S., Tian, Y., et al.: Image saliency estimation via random walk guided by informativeness and latent signal correlations. Signal Process. Image Commun. 38, 3–14 (2015)
Yangming, O., Sotiras, A., Paragios, N., et al.: DRAMMS: deformable registration via attribute matching and mutual-saliency weighting. Med. Image Anal. 15, 622–639 (2011)
Yaman, M., Kalkan, S.: An iterative adaptive multi-modal stereo-vision method using mutual information. J. Vis. Commun. Image Representation 26, 115–131 (2015)
Wang, Z., Bovik, A.C., Sheikh, H.R., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)
Hou, W., Gao, X., Tao, D., et al.: Visual saliency detection using information divergence. Pattern Recognit. 46, 2658–2669 (2013)
Sun, J., Jiangchuan Xie, J., Liu, J., et al.: Image adaptation and dynamic browsing based on two-layer saliency combination. IEEE Trans. Broadcast. 59, 602–613 (2013)
Fellrath, J., Ptak, R.: The role of visual saliency for the allocation of attention: evidence from spatial neglect and hemianopia. Neuropsychologia 73, 70–81 (2015)
Ren, J., Jiang, X., Yuan, J.: Learning LBP structure by maximizing the conditional mutual information. Pattern Recognit. 48, 3180–3190 (2015)
Acknowledgements
This work was supported in part by the National Nature Science Foundation of China (Nos. 61473077, 61473078, 61503075), the Key Project of the National Nature Science Foundation of China (No. 61134009), Cooperative research funds of Hong Kong and Macao scholars (No. 61428302) and the National Natural Science Funds Overseas, Shanghai Pujiang Program (15PJ1400100), Specialized Research Fund for Shanghai Leading Talents, Program for Changjiang Scholars from the Ministry of Education, Innovation Program of Shanghai Municipal Education Commission (No. 14ZZ067), and the Fundamental Research Funds for the Central Universities (Nos. 15D110423, 2232015D3-32), Project of the Shanghai Committee of Science and Technology (No. 13JC1407500), Provincial Natural Science Research Project of Anhui province higher education promotion plan (TSKJ2014B06) and Provincial Natural Science major project of Anhui Province (KJ2015ZD06).
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Dou, Y., Hao, K., Ding, Y. (2016). Weighted Feature Correlation and Fusion Saliency. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 646. Springer, Singapore. https://doi.org/10.1007/978-981-10-2672-0_8
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DOI: https://doi.org/10.1007/978-981-10-2672-0_8
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