Vats, E. and Lim, C.K. and Chan, C.S. (2015) An improved BK sub-triangle product approach for scene classification. Journal of Intelligent & Fuzzy Systems, 29 (5). pp. 1923-1931. ISSN 1064-1246, DOI https://doi.org/10.3233/IFS-151670.
Full text not available from this repository.Abstract
Scene classification is a popular research topic in computer vision, and has received much attention in the recent past. Conventionally, scene classes are considered to be mutually exclusive. However, in real-world scenarios a scene image may belong to multiple classes, depending upon different perceptions of the masses. In this paper, we propose an improved Bandler and Kohout's sub-triangle product (BK subproduct) approach to address this issue. Instead of using the original BK subproduct solely, we introduce a combination of inference structures. The advantages are three-fold. Firstly, using the BK subproduct as an inference engine, we are able to attain the relationships between image data sets and scene classes that are not directly associated. Secondly, our approach is able to model non-mutually exclusive data, as opposed to conventional solutions. Finally, our classification result is not binary. Instead, we can classify each scene image as belonging to multiple distinct scene classes. Experimental results on public datasets demonstrate the effectiveness of the proposed method.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | BK sub-triangle product; Scene classification; Inference structure; Fuzzy implication operator |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 01 Oct 2018 05:05 |
| Last Modified: | 01 Oct 2018 05:05 |
| URI: | http://eprints.um.edu.my/id/eprint/19503 |
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