Multi-objective whale optimization algorithm for content-based image retrieval View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-10

AUTHORS

Mohamed Abd El Aziz, Ahmed A. Ewees, Aboul Ella Hassanien

ABSTRACT

In the recent years, there are massive digital images collections in many fields of our life, which led the technology to find methods to search and retrieve these images efficiently. The content-based is one of the popular methods used to retrieve images, which depends on the color, texture and shape descriptors to extract features from images. However, the performance of the content-based image retrieval methods depends on the size of features that are extracted from images and the classification accuracy. Therefore, this problem is considered as a multi-objective and there are several methods that used to manipulate it such as NSGA-II and NSMOPSO. However, these methods have drawbacks such as their time and space complexity are large since they used traditional non-dominated sorting methods. In this paper, a new non-dominated sorting based on multi-objective whale optimization algorithm is proposed for content-based image retrieval (NSMOWOA). The proposed method avoids the drawbacks in other non-dominated sorting multi-objective methods that have been used for content-based image retrieval through reducing the space and time complexity. The results of the NSMOWOA showed a good performance in content-based image retrieval problem in terms of recall and precision. More... »

PAGES

26135-26172

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11042-018-5840-9

DOI

http://dx.doi.org/10.1007/s11042-018-5840-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101558598


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