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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.

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Authors and Affiliations

  1. LRIA Laboratory, Computer Science Department, USTHB University, Algiers, Algeria

    Hadjer Lacheheb & Saliha Aouat

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  1. Hadjer Lacheheb
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  2. Saliha Aouat
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Correspondence to Hadjer Lacheheb.

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Lacheheb, H., Aouat, S. SIMIR: New mean SIFT color multi-clustering image retrieval. Multimed Tools Appl 76, 6333–6354 (2017). https://doi.org/10.1007/s11042-015-3167-3

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