Abstract
In this paper we present a novel image classification framework, which is able to automatically re-configure and adapt its feature-driven classifiers and improve its performance based on user interaction during on-line processing mode. Special emphasis is placed on the generic applicability of the framework to arbitrary surface inspection systems. The basic components of the framework include: recognition of regions of interest (objects), adaptive feature extraction, dealing with hierarchical information in classification, initial batch training with redundancy deletion and feature selection components, on-line adaptation and refinement of the classifiers based on operators’ feedback, and resolving contradictory inputs from several operators by ensembling outputs from different individual classifiers. The paper presents an outline on each of these components and concludes with a thorough discussion of basic and improved off-line and on-line classification results for artificial data sets and real-world images recorded during a CD imprint production process.
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
Demant, C., Streicher-Abel, B., Waszkewitz, P.: Industrielle Bildverarbeitung. Springer, Heidelberg (1998)
Hayes, B.: Fully automatic color print inspection by digital image processing systems. In: Becker, M., Daniel, R.W., Loffeld, O. (eds.) Proceedings SPIE, vol. 2247, pp. 235–244 (1994)
Silven, O., Niskanen, M.: Framework for industrial visual surface inspection. In: Proc. 6th International Conference on Quality Control by Artificial Vision, pp. 1–6 (2003)
Kyong, I., Bowyer, K., Sivagurunath, M.: Evaluation of texture segmentation algorithms. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 1999), p. 1294 (1999)
Caleb-Solly, P., Smith, J.: Adaptive surface inspection via interactive evolution. Image and Vision Computing 25(7), 1058–1072 (2007)
Mikolajczyk, K., Schmid, K.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63–86 (2004)
Gan, G., Ma, C., Wu, J.: Data Clustering: Theory, Algorithms, and Applications (Asa-Siam Series on Statistics and Applied Probability). In: Society for Industrial & Applied Mathematics, USA (2007)
Duda, R., Hart, P., Stork, D. (eds.): Pattern Classification - Second Edition, England. West Sussex PO 19 8SQ. Wiley-Interscience, Chichester (2000)
Kim, C., Koivo, A.: Hierarchical classification of surface defects on dusty wood boards. Pattern Recognition Letters 15, 712–713 (1994)
Stockman, G., Shapiro, L.: Computer Vision. Prentice-Hall, Englewood Cliffs (2001)
Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)
Chen, H., Liu, T., Fuh, C.: Probabilistic tracking with adaptive feature selection. In: Proc. of 17th International Conference on Pattern Recognition (ICPR 2004), vol. 2, pp. 736–739 (2004)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)
Kudo, M., Sklansky, J.: Comparison of algorithms that select features for pattern classifiers. Pattern Recognition 33, 25–41 (2000)
Tahir, M.A., Kurugollu, A.B., F.: Simultaneous feature selection and feature weighting using hybrid tabu search/k-nearest neighbor classifier. Pattern Recognition Letters 28 (2007)
Quinlan, J.R.: C4.5: Programs for Machine Learning, USA. Morgan Kaufmann Publishers Inc, San Francisco (1993)
Breiman, L., Friedman, J., Stone, C., Olshen, R.: Classification and Regression Trees. Chapman and Hall, Boca Raton (1993)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, Heidelberg (2001)
Lughofer, E.: Extensions of vector quantization for incremental clustering. In: Pattern Recognition (to appear, 2007), doi:10.1016/j.patcog.2007.07.19
Hansen, L., Salamon, P.: Neural network ensembles. IEEE Transactions on Patterns Analysis and Machine Intelligence 12, 993–1001 (1990)
Woods, K., Kegelmeyer, W.P., Bowyer, K.: Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 405–410 (1997)
Kuncheva, L.I.: Combining pattern classifiers: Methods and algorithms. Wiley, Chichester (2004)
Sannen, D., Van Brussel, H., Nuttin, M.: Classifier fusion using discounted dempster-shafer combination. In: Poster Proceedings of International Conferernce on Machine Learning and Data Mining, pp. 216–230 (2007)
Tahir, M.A., Smith, J.: Improving nearest neighbor classifier using tabu search and ensemble distance metrics. In: Proceedings of the Sixth International Conference on Data Mining (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sannen, D. et al. (2008). An On-Line Interactive Self-adaptive Image Classification Framework. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-79547-6_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79546-9
Online ISBN: 978-3-540-79547-6
eBook Packages: Computer ScienceComputer Science (R0)