Abstract
Based on the salient object of human visual perception inconsistent scale, this paper proposes a multi-scale spectrum visual saliency perception with hypercomplex discrete cosine transform. In hypercomplex image color space to build parallel computing model, the method use HDCT to extract local spectral feature in an image. Meanwhile, the sparse energy spectrum calculated by hypercomplex discrete cosine transform on local image region was taken as visual stimulation signal. Then a visual saliency measurement was taken on both this region and its neighbor regions. Finally, the multi-sacle normalization was on the visual saliency response. The subjective and objective experimental results on the public saliency perception datasets demonstrated that both the precision and time cost of the proposed approach were better than the other state-of the-art approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Borji, A., Itti, L.: State-of-the-art in visual attention modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 185–207 (2013)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)
Bruce, N.D., Tsotsos, J.K.: Saliency, attention, and visual search: an information theoretic approach. J. Vis. 9(3), 1–24 (2009)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, Montreal, Quebec, Canada, pp. 545–552 (2006)
Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: Proceedings of ACM International Conference on Multimedia, pp. 374–381 (2003)
Gao, D.S., Han, S.Y., Vasconcelos, N.: Discriminant saliency, the detection of suspicious coincidences and applications to visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 989–1005 (2009)
Liu, T., Yuan, Z.J., Sun, J., et al.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353–367 (2011)
Hou, X.D., Zhang, L.Q.: Saliency detection: a spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA, pp. 2280–2287. IEEE (2007)
Guo, C.L., Zhang, L.M.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 19(1), 185–198 (2010)
Li, J., Levine, M.D., An, X.J., et al.: Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 996–1010 (2013)
Fang, Y.M., Lin, W.S., Lee, B.S., Lau, C.T., Chen, Z.Z., Lin, C.W.: Bottom-up saliency detection model based on human visual sensitivity and amplitude spectrum. IEEE Trans. Multimedia 14(1), 187–198 (2012)
Schauerte, B., Stiefelhagen, R.: Predicting human gaze using quaternion DCT image signature saliency and face detection. In: IEEE Workshop on the Applications of Computer Vision, Breckenridge, Colorado, USA, pp. 137–144. IEEE (2012)
Navalpakkam, V., Itti, L.: An integrated model of top-down and bottom-up attention for optimizing detection speed. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2049–2056 (2006)
Cerf, M., Harel, J., Wolf, E., et al.: Predicting human gaze using low-level saliency combined with face detection. In: Advances in Neural Information Processing Systems, pp. 241–248 (2007)
Abdollahian, G., Delp, E.J.: Finding regions of interest in home videos based on camera motion. In: IEEE International Conference on Image Processing, pp. 545–548 (2007)
Wei, F., Hu, B.: Quaternion discrete cosine transform and its application in color template matching. In: International Congress on Image and Signal Processing, Sanya, Hainan, China, pp. 252–256. IEEE (2008)
Acknowledgment
The paper was supported in part by the National Natural Science Foundation (NSFC) of China under Grant Nos. (61365003, 61302116), National High Technology Research and Development Program of China No. 2013AA014601, China Postdoctoral Science Foundation (2014M550494), and Gansu Province Basic Research Innovation Group Project (1506RJIA031).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Xiao, L., Li, C., Hu, Z., Pan, Z. (2016). Multi-scale Spectrum Visual Saliency Perception via Hypercomplex DCT. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_58
Download citation
DOI: https://doi.org/10.1007/978-3-319-42294-7_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42293-0
Online ISBN: 978-3-319-42294-7
eBook Packages: Computer ScienceComputer Science (R0)