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Artificial bee colony algorithm for content‐based image retrieval
Author(s) -
Banharnsakun Anan
Publication year - 2020
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12275
Subject(s) - computer science , content based image retrieval , artificial bee colony algorithm , image retrieval , artificial intelligence , field (mathematics) , metaheuristic , pattern recognition (psychology) , image (mathematics) , process (computing) , digital image , machine learning , image processing , mathematics , pure mathematics , operating system
Image retrieval is the process of searching for digital images from a large database. There exist two distinctive research groups, which employ the content‐based and description‐based approaches, respectively. However, research in the content‐based domain is currently dominant in the field, while the other approaches are not as widely utilized. Although there are a number of different techniques that are available for image retrieval, the development of more effective methods is still necessary. In recent years, previous research has shown that biologically inspired metaheuristic algorithms have great potential for use in solving problems in many science and engineering domains. The artificial bee colony (ABC) algorithm is one of the more promising biologically inspired metaheuristic approaches used to find optimal solutions as it has the advantages of convenient implementation and efficient performance. In this article, a new efficient method based on a combination of the gray‐level cooccurrence matrix (GLCM) with the ABC, referred to as “GLCM‐ABC,” is proposed for use in content‐based image retrieval (CBIR). The experimental results demonstrate that the proposed approach works well for CBIR and can classify specific types of material surfaces in images with a reasonably high level of accuracy as well as outperform other existing algorithms.