Multimedia Tools and Applications

, Volume 76, Issue 5, pp 6333–6354 | Cite as

SIMIR: New mean SIFT color multi-clustering image retrieval

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Abstract

Content based image retrieval systems (CBIR) are used to search images on the basis of their visual content in a huge image database. This approach uses a multi-clustering technique with a multi-searching process. In addition it integrate, Mean SIFT (Scale Invariant Feature Transform) descriptor as local feature and HSV(hue, saturation, value) histogram as global feature. Our proposition aims to a maximum separation of the execution to gain time and keep the performance of each descriptor. Local and global features are combined for more relevant results. Getting several views to relevant results to cover the subjectivity in displaying results. This article, detailed our proposed method with a comparison with the FIRE (Flexible Image Retrieval) Engine and LIRE (Lucene Image Retrieval). The results demonstrate the feasibility and relevance of our proposition.

Keywords

CBIR SIFT Mean SIFT HSV Multi-clustering 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.LRIA Laboratory, Computer Science DepartmentUSTHB UniversityAlgiersAlgeria

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