Abstract
General image segmentation is a non–trivial task, which requires significant computational power and huge amount of knowledge incorporated. Fortunately, it is not necessary in all the cases. In some specific cases, simpler non–supervised or supervised segmentation methods can be used giving even better results. In this paper, a novel trainable segmentation method based on RapidMiner data–mining platform is introduced, and its functionality is described. The method implementation was released under open–source license as a part of IMMI (IMage MIning) extension of the RapidMiner platform. When compared to other trainable segmentation algorithms, the platform provides flexibility connected with all the features of one of the most widely used data–mining platform today. The functionality has been verified on the satellite image use–case, accuracy achieving 78.3% pixel error.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Burget, R., Cika, P., Zukal, M., Masek, J.: Automated Localization of Temporomandibular Joint Disc in MRI Images. In: International Conference on Telecommunications and Signal Processing (TSP), pp. 413–416 (2011) ISBN: 978-1-4577-1409-2
Riha, K., Masek, J., Burget, R., Benes, R., Zavodna, E.: Novel Method for Localization of Common Carotid Artery Transverse Section in Ultrasound Images Using Modified Viola–Jones Detector. Ultrasound in Medicine and Biology (2013)
Masek, J., Burget, R., Karasek, J., Uher, V., Güney, S.: Evolutionary Improved Object Detector for Ultrasound Images. In: International Conference on Telecommunications and Signal Processing, TSP (2013)
Masek, J., Burget, R., Uher, V., Güney, S.: Speeding up Viola–Jones Algorithm Using Multi–Core GPU Implementation. In: International Conference on Telecommunications and Signal Processing, TSP (2013)
Sommer, C., Straehle, C., Kothe, U., Hamprecht, F.: Ilastik: Interactive Learning and Segmentation Toolkit. In: Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, Illinois, USA, pp. 230–233. IEEE, ISBI (2011)
Friedland, G., Jantz, K., Rojas, R.: SIOX: Simple Interactive Object Extraction in Still Images. In: Seventh IEEE International Symposium on Multimedia (2005)
Bai, X., Sapiro, G.: A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting. In: IEEE ICCV, pp. 1–8 (2007)
Boykov, Y., Jolly, M.: Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N–D Images. In: IEEE ICCV, pp. 105–112 (2001)
Kolmogorov, V., Boykov, Y.: What Metrics Can Be Approximated by Geo–Cuts, or Global Optimization of Length/Area and Flux. In: IEEE ICCV, vol. 1, pp. 564–571 (2005)
Price, B., Morse, B., Cohen, S.: Geodesic Graph Cut for Interactive Image Segmentation. In: IEEE CVPR, pp. 3161–3168 (2010)
Vicente, S., Kolmogorov, V., Rother, C.: Graph Cut Based Image Segmentation with Connectivity Priors. In: IEEE CVPR (2008)
Sinop, A., Grady, L.: A Seeded Image Segmentation Framework Unifying Graph Cuts and Random Walker which Yields a New Algorithm. In: IEEE ICCV (2007)
Grady, L.: Random Walks for Image Segmentation. IEEE Trans. PAMI 28(11), 1768–1783 (2006)
Couprie, C., Grady, L., Najman, L., Talbot, H.: Power watersheds: A New Image Segmentation Framework Extending Graph Cuts, Random Walker and Optimal Spanning Forest. In: IEEE ICCV (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Masek, J., Burget, R., Uher, V. (2013). IMMI: Interactive Segmentation Toolkit. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-41013-0_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41012-3
Online ISBN: 978-3-642-41013-0
eBook Packages: Computer ScienceComputer Science (R0)