Platform for evaluation of image classifiers
Authors: 
Jana Šilhavá, Vítězslav Beran, Petr Chmelař, Adam Herout, Michal Hradiš, Roman Juránek, Pavel ZemčíkAuthors Info & Claims
Pages 93 - 99
Abstract
Classifiers used in image processing and computer vision are frequent subject of research and exploitation in applications. This contribution does not directly involve research in the classification itself but rather introduces a systematic approach of evaluation of image classifiers, comparison between the classifiers, and "tuning" the classifiers for particular applications. The proposed approach is included in an open software system for evaluation of the image classifiers. The contribution also demonstrates application of the system on several selected classifiers and discusses the possibilities and results.
References
[1]
Bartlett, M. et al. 2005. Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2, 568--573.
[2]
Boser, B. E., Guyon, I. M., Vapnik, V. N. 1992. A Training Algorithm for Optimal Margin Classifiers. In 5th Annual ACM Workshop on COLT.
[3]
Bridle, J., S. 1990. Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition. In Neurocomputing: Algorithms, Architectures and Applications, F. Fogelman Soulié and J. Hérault Eds., NATO ASI Series, 227--236.
[4]
Cantoni, V., Petrosino, A. 2000. 2-D Object Recognition by Structured Neural Networks in a Pyramidal Architecture. In Proceeding of the Fifth IEEE International Workshop on Computers Architectures for Machine Perception (CAMP 00), 0-7695-0740-9/00.
[5]
CBCL at MIT. 2005. CBCL face dataset, http://cbcl.mit.edu/software-datasets/FaceData2.html.
[6]
Chang, C., Lin, C. 2001. LIBSVM: A Library for Support Vector Machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[7]
Egan, J., P. 1975. Signal Detection Theory and ROC Analysis. Academic Press.
[8]
Farber, P. 1997. Quicknet on MultiSpert: Fast Parallel Neural Network Training. International Computer Science Institute, Berkeley, TR-TR-97-047.
[9]
Freund, Y., Schapire, R. 1997. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. In Journal of Computer and System Sciences, 55(1):119--139.
[10]
Gllavata, J., Ewerth, R., Freisleben, B. 2004. A Text Detection, Localization and Segmentation System for OCR in Images. In IEEE, Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering (ISMSE 04), 0-7695-2217-3/04.
[11]
Gosselin, P. H., Cord, M. 2004. A Comparison of Active Classification Methods for Content-Based Image Retrieval. In ACM Proceedings of the 1st international workshop on Computer vision meets databases.
[12]
Han, J., Kamber, M. 2006. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, ISBN 1558609016.
[13]
Howley, T., and Madden, M. 2005. The Genetic Kernel Support Vector Machine: Description and Evaluation. In Artificial Intelligence Review. http://www.icsi.berkeley.edu/~dpwe/projects/sprach/sprachcore.html
[14]
ISCI. 2004. The SPRACHcore Software Package.
[15]
Li, S. et al. 2002. FloatBoost Learning for Classification. In: S. Thrun S. Becker and K. Obermayer, editors, NIPS 15. MIT Press.
[16]
McCulloch, W., S., Pitts, W. 1943. A Logical Calculus of the Ideas Imminent in Nervous Activity. In: Bulletin of Mathematical Biophysics, 5 115--133.
[17]
Mierswa, I. at al. 2006. YALE: Rapid Prototyping for Complex Data Mining Tasks. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[18]
Minsky, M., Papert, S. 1969. Perceptrons. MIT Press, Cambridge, Mass.
[19]
Molina, L. C., Belanche, L., Nebot, A. 2002. Feature Selection Algorithms: A Survey and Experimental Evaluation. In: IEEE International Conference on Data Mining 2002.
[20]
Ney, H. 1995. On the Probabilistic Interpretation of Feedforward Classification Network. In: IEEE Transactions on Pattern Analysis and Machine Inteligence, Vol. 17, NO. 2.
[21]
Rosenblatt, R., 1959. Principles of Neurodynamics. New York, Spartan Books.
[22]
Rumelhart, D., E., Hinton, G., E., Williams, R., J. 1986. Learning Internal Representations by Error Propagation. In: Parallel Distributed Processing: Exprorations in the Microstructure of Cognition. Vol. 1: Foundations. MIT Press.
[23]
Schapire, R., Singer, Y. 1999. Improved Boosting Algorithms Using Confidence-Rated Predictions. In: Machine Learning, 37(3):297--336.
[24]
Sejnowski, T., Rosenberg, C., R. 1986. NETtalk: A Parallel Network thats Learns to Read Aloud. Johns Hopkins Univ. Technical Report JHU/EECS-86/01.
[25]
Šochman, J., Matas, J, 2004. AdaBoost with Totally Corrective Updates for Fast Face Detection. Sixth IEEE International Conference on Automatic Face and Gesture Recognition, p. 445.
[26]
Šochman, J., Matas, J. 2005. WaldBoost --- Learning for Time Constrained Sequential Detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2
[27]
Sun Microsystems. 2005. N1 Grid Engine 6 User Guide.
[28]
Theodoridis, S., Kountroumbas, K. 2003. Pattern Recognition. Academic Press, USA. ISBN: 0-12-685875-6.
[29]
UPGM. 2007. http://www.fit.vutbr.cz/research/groups/graph/
[30]
Vapnik, V. N., Lerner, A. 1963. Pattern Recognition Using Generalized Portrait Method. In: Automation and Remote Control.
[31]
Viola, P., Jones, M. 2001. Rapid Object Detection using a Boosted Cascade of Simple Feaures. In CVPR, 2001.
Index Terms
- Platform for evaluation of image classifiers
Recommendations
AdaBoost classifiers for pecan defect classification
Highlights The performance of AdaBoost algorithms were compared with support vector machine and Bayesian classifiers for pecan defect classification. AdaBoost classifiers took least time and gave best classification accuracy. AdaBoost classifiers ...
AdaBoost with SVM-based component classifiers
The use of SVM (Support Vector Machine) as component classifier in AdaBoost may seem like going against the grain of the Boosting principle since SVM is not an easy classifier to train. Moreover, Wickramaratna et al. [2001. Performance degradation in ...
Framework for research on detection classifiers
SCCG '08: Proceedings of the 24th Spring Conference on Computer GraphicsDetection of patterns in images with classifiers is currently one of the most important research topics in computer vision. Many practical applications such as face detection exist and recent work even suggests that any specialized detectors (e.g. ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
April 2007
242 pages
Copyright © 2007 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
- Comenius University: Comenius University
In-Cooperation
- SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
- EUROGRAPHICS: The European Association for Computer Graphics
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 April 2007
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
SCCG07
Sponsor:
- Comenius University
Acceptance Rates
Overall Acceptance Rate 67 of 115 submissions, 58%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 20Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 27 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in