Abstract
Images have been widely used in today’s life varying from personal usage with Flickr, Facebook to the analysis of hyperspectral images. Availability of such huge volume of images in digital form requires an automatic analysis on visual content. The major challenge in content labeling is in segmentation, which divides the image into regions. Our work focuses on the segmentation technique that adapts based on regions in the natural images. The proposed method used automatic seed selection by analyzing the dynamic color distribution of the image. The experimental results on datasets show the better performance of our method.
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
References
Raghesh Krishnan, K., Radhakrishnan, S.: Focal and diffused liver disease classification from ultrasound images based on isocontour segmentation. IET Image Process. 9, (4, 1), 261–270 (2015)
Vogel, J., Schwaninger, A.: Categorization of natural scenes: local vs global information, University of British Columbia (2001)
Kumar, D.A., Kannathasan, N.: A study and characterization of chemical properties of soil surface data using K-means algorithm. In: Proceedings of the 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME) 21–22 February, (2013). 978-1-4673-5845-3/13
Sert, E., Okumus, I.T.: Segmentation of mushroom and cap width measurement using modified K-means clustering algorithm. In: Digital Image Processing and Computer Graphics, Vol. 12, Issue 4 (2014)
Siddiqui, F.U., Isa, N.AM., Member, IEEE.: Enhanced moving K-Means (EMKM) algorithm for image segmentation. IEEE Trans. Consum. Electron. 57(2) (2011)
Raju, P.D.R. et al.: Image segmentation by using histogram thresholding. IJCSET, 2(1), 776–779 (2012)
Elmoufidi, A., Student Member IEEE, et al.: Automatically density based breast segmentation for mammograms by using dynamic k-means algorithm and seed based region growing. In: IEEE Instrumentation and Measurement Society, pp. 533–538. https://doi.org/10.1109/i2mtc.2015.7151324, 2015
Genovez, P. et al.: An image-segmentation-based framework to detect oil slicks from moving vessels in the Southern African oceans using SAR imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 99, 1–9 (2017)
Machine learning data website available http://www.archive.ics.uci.edu/ml/datasets.html
Kachouri, R., Soua, M., Akil, M.: Unsupervised image segmentation based on local pixel clustering and Low-Level region merging. In: 2nd IEEE International Conference on Advanced Technologies for Signal and Image Processing ATSIP’. Monastir, Tunisia, vol. 16, (2016)
Aarthi, R., Padmavathi, S., Amudha J.: Vehicle detection in static images using color and corner map. In: International Conference on Recent Trends in Information, Telecommunication and Computing, kochi. pp. 12–13 (2010)
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
Aarthi, R., Shanmuga Priya, S. (2020). Automated Histogram-Based Seed Selection for the Segmentation of Natural Scene. In: Elçi, A., Sa, P., Modi, C., Olague, G., Sahoo, M., Bakshi, S. (eds) Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, vol 766. Springer, Singapore. https://doi.org/10.1007/978-981-13-9683-0_30
Download citation
DOI: https://doi.org/10.1007/978-981-13-9683-0_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9682-3
Online ISBN: 978-981-13-9683-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)