Abstract
In the field of computer vision, the detection of salient object is an important step and one of the preconditions for salient object extraction. The outcome resulting from some existing detection methods for salient object is considerably different from the Ground Truth. In view of the shortcomings of existing methods, this paper proposes a saliency detection method based on the integration of global contrast and superpixels. The salience value of each pixel is measured according to the global contrast of the pixels in the image. A histogram optimization technique is used to highlight the low-contrast pixels of the salient region in the image and omit the high-contrast pixels of the background. In order to improve the image quality of the salient image, the superpixel image segmentation based on K-Means clustering algorithm is proposed, and finally, we generate a more accurate saliency map through the integration with superpixels. The experiment is performed on the public dataset MSRA10 K. The results show that the histogram optimization can help improve the contrast of the salient pixels and generate a better saliency map by integrating with superpixels. Compared with other classical algorithms, the proposed method outperforms other methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Karczmarek, P., et al.: A study in facial features saliency in face recognition: an analytic hierarchy process approach. Soft. Comput. 21(24), 7503–7517 (2017)
Hui, B., et al.: Accurate image segmentation using Gaussian mixture model with saliency map. Pattern Anal. Appl. 2, 1–10 (2018)
Yikun, Huang: Simulation of parallel fusion method for multi-feature in double channel video image. Comput. Simul. 35(4), 154–157 (2018)
Niu, Y., Chen, J., Guo, W.: Meta-metric for saliency detection evaluation metrics based on application preference. Multimed. Tools Appl. 4, 1–19 (2018)
Xue, X., Wang, Y.: Using memetic algorithm for instance coreference resolution. IEEE Trans. Knowl. Data Eng. 28(2), 580–591 (2016)
Yun, Z., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: ACM International Conference on Multimedia (2006)
Cheng, M.M., et al.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)
Niu, Y., Lin, W., Ke, X.: CF-based optimisation for saliency detection. IET Comput. Vis. 12(4), 365–376 (2018)
Ishikura, K., et al.: Saliency detection based on multiscale extrema of local perceptual color differences. IEEE Trans. Image Process. 27(2), 703 (2018)
Singh, A., Yadav, S., Singh, N.: Contrast enhancement and brightness preservation using global-local image enhancement techniques. In: Fourth International Conference on Parallel (2017)
Cuevas-Olvera, M., et al.: Salient Object Detection in Digital Images Based on Superpixels and Intrinsic Features. IEEE (2018)
Liu, S., Jiang, N., Liu, Z.: Saliency detection of infrared image based on region covariance and global feature. J. Syst. Eng. Electron. 29(3), 483–490 (2018)
Niu, Y., Su, C., Guo, W.: Salient object segmentation based on superpixel and background connectivity prior. IEEE Access 6, 56170–56183 (2018)
Acknowledgements
This work is supported by the 2018 Program for Outstanding Young Scientific Researcher in Fujian Province University, Education and Scientific Research Project for Middle-aged and Young Teachers in Fujian Province (No: JZ170367).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Huang, Y., Liu, L., Li, Y., Chen, J., Lu, J. (2020). Saliency Detection Based on the Integration of Global Contrast and Superpixels. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 157. Springer, Singapore. https://doi.org/10.1007/978-981-13-9710-3_36
Download citation
DOI: https://doi.org/10.1007/978-981-13-9710-3_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9709-7
Online ISBN: 978-981-13-9710-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)