A Content Based Image Retrieval Method Based on K-Means Clustering Technique

Mohamed Ouhda, Khalid El Asnaoui, Mohammed Ouanan and Brahim Aksasse
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Mohamed Ouhda: Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco
Khalid El Asnaoui: Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco
Mohammed Ouanan: Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco
Brahim Aksasse: Moulay Ismail University, Faculty of Sciences and technology, Department of Computer Science, ASIA Team M2I Laboratory, Meknes, Morocco

Journal of Electronic Commerce in Organizations (JECO), 2018, vol. 16, issue 1, 82-96

Abstract: With the appearance of many devices that are used in image acquisition comes a large number of images every day. The rapid access to these huge collections of images and retrieval of similar images (Query) from this huge collection of images presents major challenges and requires efficient algorithms. The main goal of the proposed system is to provide an accurate result with lower computational time. For this purpose, the authors apply a new method based on k-means clustering technique to match image's descriptors. This article provides a detailed view of the solution the authors have adopted and which perfectly meets their needs. For validation, they apply all of these techniques on two image databases in order to evaluate the performance of their system.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jeco00:v:16:y:2018:i:1:p:82-96

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