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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chern Hong Lim Chee Seng Chan |
| Copyright Year | 2012 |
| Description | Author affiliation: Centre of Image and Signal Processing, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia (Chern Hong Lim; Chee Seng Chan) |
| Abstract | Scene classification has been studied extensively in the recent past. Most of the state-of-the-art solutions assumed that scene classes are mutually exclusive. However, this is not true as a scene image may belongs to multiple classes and different people are tend to respond inconsistently even given a same scene image. In this paper, we propose a fuzzy qualitative approach to address this problem. That is, we first adopted the fuzzy quantity space to model the training data. Secondly, we present a novel weight function, w to train a fuzzy qualitative scene model in the fuzzy qualitative states. Finally, we introduce fuzzy qualitative partition to perform the scene classification. Empirical results using a standard dataset and a comparison with K-nearest neighbour has shown the effectiveness and robustness of the proposed method. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 2109654 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467315074 |
| ISSN | 10987584 |
| e-ISBN | 9781467315067 |
| e-ISBN | 9781467315050 |
| DOI | 10.1109/FUZZ-IEEE.2012.6251230 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training data Classification algorithms Data models Training Testing Computational modeling Support vector machines |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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