Double Probability Model for Open Set Problem at Image Classification

Papp, Dávid; Szűcs, Gábor

doi:10.15388/Informatica.2018.171

Informatica

Double Probability Model for Open Set Problem at Image Classification

Volume 29, Issue 2 (2018), pp. 353–369

Dávid Papp Gábor Szűcs

https://doi.org/10.15388/Informatica.2018.171

Pub. online: 1 January 2018 Type: Research Article

Open Access

Received
1 May 2017

Accepted
1 January 2018

Published
1 January 2018

Abstract

In this paper an exploratory classification, so called open set problem, is investigated. Open set recognition assumes there is incomplete knowledge of the world at training time, and unknown classes can be submitted to an algorithm during testing. For this problem we elaborated a theoretical model, Double Probability Model (DPM), based on likelihoods of a classifier. We developed it with double smoothing solution in order to solve technical difficulties avoiding zero values in the predictions. We applied the GMM based Fisher vector for the mathematical representation of the images and the C-SVC with RBF kernel for the classification. The last contributions of the paper are new goodness indicators for classification in open set problem, the new type of accuracies. The experimental results present that our Double Probability Model helps with classification, the accuracy increases by using our proposed model. We compared our method to a state-of-the-art open set recognition solution and the results showed that DPM outperforms existing techniques.

References

Bauml, M., Tapaswi, M., Stiefelhagen, R. (2013). Semi-supervised learning with constraints for person identification in multimedia data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3602–3609.

Bendale, A., Boult, T. (2015). Towards open world recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1893–1902.

Boser, B., Guyon, I., Vapnik, V. (1992). A training algorithm for optimal margin classifier. In: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pp. 144–152.

Cevikalp, H., Triggs, B. (2012). Efficient object detection using cascades of nearest convex model classifiers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893.

Chang, C.-C., Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 27.1–27.27.

Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A. (2011). The devil is in the details: an evaluation of recent feature encoding methods. In: Proceedings of the 22nd British Machine Vision Conference, pp. 76.1–76.12.

Cortes, C., Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

Dempster, A., Laird, N., Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1), 1–38.

Fei-Fei, L., Fergus, R., Perona, P. (2004). Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Generative-Model Based Vision.

Fei-Fei, L., Fergus, R., Torralba, A. (2007). Recognizing and learning object categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Harris, C., Stephens, M. (1988). A combined corner and edge detector. In: Proceedings of the Alvey Vision Conference, pp. 23.1–23.6.

Huang, T.-K., Weng, R.C., Lin, C.-J. (2006). Generalized Bradley–Terry models and multi-class probability estimates. Journal of Machine Learning Research, 7, 85–115.

Jain, L.P., Scheirer, W.J., Boult, T.E. (2014). Multi-class open set recognition using probability of inclusion. In: European Conference on Computer Vision, pp. 393–409.

Lazebnik, S., Schmid, C., Ponce, J. (2006). Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 2169–2178.

Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297.

Mikolajczyk, K., Schmid, C. (2004). Scale & affine invariant interest point detectors. International Journal of Computer Vision, 60(1), 63–86.

Perronnin, F., Dance, C. (2007). Fisher kernel on visual vocabularies for image categorization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

Platt, J. (2000). Probabilistic outputs for support vector machines and comparison to regularize likelihood methods. In: Advances in Large Margin Classifiers, pp. 61–74.

Reynolds, D.A. (2009). Gaussian mixture models. In: Encyclopedia of Biometric Recognition, pp. 659–663.

Scheirer, W., Rocha, A., Sapkota, A., Boult, T.E. (2013). Towards open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(7), 1757–1772.

Scheirer, W.J., Jain, L.P., Boult, T.E. (2014). Probability models for open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(11), 2317–2324.

Schölkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.

Szűcs, G., Henk, Zs. (2015). Active clustering based classification for cost effective prediction in few labeled data problem. Economy Informatics, 15(1), 5–13.

Szűcs, G., Marosvári, B. (2015). Half and fully automatic character identification in movies based on face detection. Acta Universitatis Sapientiae, Electrical and Mechanical Engineering, 7, 80–92.

Tax, D.M.J., Duin, R.P.W. (2004). Support vector data description. Machine Learning, 54, 45–66.

Tomasi, C. (2004). Estimating Gaussian Mixture Densities with EM – A Tutorial. Technical report, Duke University.

Zhang, R., Metaxas, D. (2006). RO-SVM: Support vector machine with reject option for image categorization. In: Proceedings of the 17th British Machine Vision Conference, pp. 1209–1218.

Zhu, X., Goldberg, A.B. (2009). Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 3(1), 1–130.

Biographies

Papp Dávid

pappd@tmit.bme.hu

D. Papp was born in 1990 in Hungary and he has received BSc and MSc in computer science (at specialization of media informatics) from Budapest University of Technology and Economics (BME) and now he is a PhD student in computer science at the same university.

Szűcs Gábor

szucs@tmit.bme.hu

G. Szűcs was born in 1970 in Hungary. He has received MSc in electrical engineering and PhD in computer science from Budapest University of Technology and Economics (BME) in 1994 and in 2002, respectively. His research areas are data, multimedia mining, content based image retrieval, semantic search. He is an associate professor at Department of Telecommunications and Media Informatics of BME. The number of his publications is more than 80. He is the president of the Hungarian Simulation Society (EUROSIM), he is the leader of the research group DCLAB (Data Science and Content Technologies). He has earned János Bolyai Research Scholarship of the Hungarian Academy of Science some years ago.

Full article Cited by

Open access article under the CC BY license.

Keywords

open world problem open set image classification unknown class double probability model

Metrics

since January 2020

1226 Article info views	650 Full article views
528 PDF downloads	225 XML downloads

RSS

Authors

Abstract

References

Biographies

Export citation

Copy and paste formatted citation

Download citation in file