A Comparative Study of Classical Clustering Method and Cuckoo Search Approach for Satellite Image Clustering: Application to Water Body Extraction
Image clustering is a critical and essential component of image analysis to several fields and could be considered as an optimization problem. Cuckoo Search (CS) algorithm is an optimization algorithm that simulates the aggressive reproduction strategy of some cuckoo species. In this paper, a combin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-01-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2018.1451214 |
| Summary: | Image clustering is a critical and essential component of image analysis to several fields and could be considered as an optimization problem. Cuckoo Search (CS) algorithm is an optimization algorithm that simulates the aggressive reproduction strategy of some cuckoo species. In this paper, a combination of CS and classical algorithms (KM, FCM, and KHM) is proposed for unsupervised satellite image classification. Comparisons with classical algorithms and also with CS are performed using three cluster validity indices namely DB, XB, and WB on synthetic and real data sets. Experimental results confirm the effectiveness of the proposed approach. |
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| ISSN: | 0883-9514 1087-6545 |