Abstract
A high quality labeled training set is necessary for any supervised machine learning algorithm. Labeling of the data can be a very expensive process, specially while dealing with data of high variability and complexity. A good example of such data are the videos from Wireless Capsule Endoscopy. Building a representative WCE data set means many videos to be labeled by an expert. The problem that occurs is the data diversity, in the space of the features, from different WCE studies. That means that when new data arrives it is highly probable that it will not be represented in the training set, thus getting a high probability of performing an error when applying machine learning schemes. In this paper an interactive labeling scheme that allows reducing expert effort in the labeling process is presented. It is shown that the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of the WCE video with less than 100 clicks.
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
Preview
Unable to display preview. Download preview PDF.
References
Bashar, M., et al.: Automatic detection of informative frames from wireless capsule endoscopy images. Medical Image Analysis 14(3), 449–470 (2010)
Coimbra, M., Cunha, J.: MPEG-7 visual descriptors: Contributions for automated feature extraction in capsule endoscopy. IEEE TCSVT 16(5), 628–637 (2006)
Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proc. of the 25th ICVLDB, VLDB 1999, pp. 518–529 (1999)
Igual, L., et al.: Automatic discrimination of duodenum in wireless capsule video endoscopy. In: IFMBE Proceedings, vol. 22, pp. 1536–1539 (2008)
Jung, Y., et al.: Active blood detection in a high resolution capsule endoscopy using color spectrum transformation. In: ICBEI, pp. 859–862 (2008)
Kang, J., Doraiswami, R.: Real-time image processing system for endoscopic applications. In: IEEE CCECE 2003, vol. 3, pp. 1469–1472 (2003)
Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Gaussian processes for object categorization. Int. J. Comput. Vision 88, 169–188 (2010)
Malagelada, C., et al.: New insight into intestinal motor function via noninvasive endoluminal image analysis. Gastroenterology 135(4), 1155–1162 (2008)
Orabona, F., Keshet, J., Caputo, B.: The projectron: a bounded kernel-based perceptron. In: Proc. of the 25th ICML 2008, pp. 720–727 (2008)
Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison (2009)
Vilarino, F., et al.: Intestinal motility assessment with video capsule endoscopy: Automatic annotation of phasic intestinal contractions. IEEE TMI 29(2), 246–259 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Drozdzal, M., Seguí, S., Malagelada, C., Azpiroz, F., Vitrià, J., Radeva, P. (2011). Interactive Labeling of WCE Images. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-21257-4_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
Online ISBN: 978-3-642-21257-4
eBook Packages: Computer ScienceComputer Science (R0)