A fuzzy qualitative approach for scene classification
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 giv...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/14091/1/426.pdf |
| _version_ | 1825720468401815552 |
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| author | Lim, C.H. Chan, C.S. |
| author_facet | Lim, C.H. Chan, C.S. |
| author_sort | Lim, C.H. |
| collection | UM |
| description | 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 data set and a comparison with K-nearest neighbour has shown the effectiveness and robustness of the proposed method.
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| first_indexed | 2024-03-06T05:35:36Z |
| format | Conference or Workshop Item |
| id | um.eprints-14091 |
| institution | Universiti Malaya |
| language | English |
| last_indexed | 2024-03-06T05:35:36Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | um.eprints-140912015-09-22T00:06:03Z http://eprints.um.edu.my/14091/ A fuzzy qualitative approach for scene classification Lim, C.H. Chan, C.S. T Technology (General) 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 data set and a comparison with K-nearest neighbour has shown the effectiveness and robustness of the proposed method. 2012-06 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.um.edu.my/14091/1/426.pdf Lim, C.H. and Chan, C.S. (2012) A fuzzy qualitative approach for scene classification. In: World Congress on Computational Intelligence , 10-15 June 2012, Brisbane, Australia. |
| spellingShingle | T Technology (General) Lim, C.H. Chan, C.S. A fuzzy qualitative approach for scene classification |
| title | A fuzzy qualitative approach for scene classification |
| title_full | A fuzzy qualitative approach for scene classification |
| title_fullStr | A fuzzy qualitative approach for scene classification |
| title_full_unstemmed | A fuzzy qualitative approach for scene classification |
| title_short | A fuzzy qualitative approach for scene classification |
| title_sort | fuzzy qualitative approach for scene classification |
| topic | T Technology (General) |
| url | http://eprints.um.edu.my/14091/1/426.pdf |
| work_keys_str_mv | AT limch afuzzyqualitativeapproachforsceneclassification AT chancs afuzzyqualitativeapproachforsceneclassification AT limch fuzzyqualitativeapproachforsceneclassification AT chancs fuzzyqualitativeapproachforsceneclassification |