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Double Probability Model for Open Set Problem at Image Classification
Volume 29, Issue 2 (2018), pp. 353–369
Pub. online: 1 January 2018      Type: Research Article      Open accessOpen 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.

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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.


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© 2018 Vilnius University
Open access article under the CC BY license.

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

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