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System profiles in content-based image indexing and retrieval

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Abstract

In this paper, a novel study on system profiles and adaptation of parameters for end-users of content-based indexing and retrieval (CBIR) applications are presented. The main objective of the study is improving the overall CBIR application performance in different hardware platforms having different technical capabilities and conditions. We define CBIR system profiles in terms of hardware and system platform attributes and propose CBIR parameters for each profile. Hence, the study consists of two main parts: system profiling and adaptation of indexing and retrieval parameters for each profile. The proposed CBIR parameters are appropriate configurations for optimal CBIR use on every platform. The proposed parameters for each system profile are assessed over a large set of experiments. Experimental studies show that the proposed parameters for each system profile have satisfactory semantic retrieval performance, with reduced computational complexity and storage space requirement. 45 to 78% improvement is achieved in the computational complexity of the retrieval process depending on the profile.

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Authors and Affiliations

  1. Department of Signal Processing, Tampere University of Technology, P.O. Box 553, 3310, Tampere, Finland

    Esin Guldogan & Moncef Gabbouj

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  1. Esin Guldogan
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  2. Moncef Gabbouj
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Correspondence to Esin Guldogan.

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Guldogan, E., Gabbouj, M. System profiles in content-based image indexing and retrieval. SIViP 4, 463–480 (2010). https://doi.org/10.1007/s11760-009-0137-0

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