Abstract
In this book, we have dived into classical and new tasks in visual saliency analysis. We presented methods belonging to two typical methodologies in computer vision: one based on image processing and algorithm design, and the other based on machine learning.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Lawrence Zitnick, C., and Parikh, D. Vqa: Visual question answering. In IEEE International Conference on Computer Vision (ICCV) (2015).
Lempitsky, V., Kohli, P., Rother, C., and Sharp, T. Image segmentation with a bounding box prior. In IEEE International Conference on Computer Vision (ICCV) (2009).
Rother, C., Kolmogorov, V., and Blake, A. Grabcut: Interactive foreground extraction using iterated graph cuts. In ACM transactions on graphics (TOG) (2004).
Tulving, E., and Schacter, D. L. Priming and human memory systems. Science 247, 4940 (1990), 301–306.
Zhang, D., Fu, H., Han, J., and Wu, F. A review of co-saliency detection technique: Fundamentals, applications, and challenges. arXiv preprint arXiv:1604.07090 (2016).
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Zhang, J., Malmberg, F., Sclaroff, S. (2019). Conclusion and Future Work. In: Visual Saliency: From Pixel-Level to Object-Level Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-04831-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-04831-0_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04830-3
Online ISBN: 978-3-030-04831-0
eBook Packages: Computer ScienceComputer Science (R0)