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Automated Benchmarking in Content-based Image Retrieval

Published in Proceedings of the 2001 IEEE International Conference on Multimedia and Expo, ICME2001. Tokyo (Japan) - 22-25 Aug. 2001 - . 2001
Abstract Benchmarking has always been a crucial problem in content-based image retrieval (CBIR). A key issue is the lack of a common access method to retrieval systems, such as SQL for relational databases. The Multimedia Retrieval Mark-up Language (MRML) solves this problem by standardizing access to CBIR systems (CBIRSs). Other difficult problems are also shortly addressed, such as obtaining relevance judgments and choosing a database for performance comparison. In this article we present a fully automated benchmark for CBIRSs based on MRML, which can be adapted to any image database and almost any kind of relevance judgment. The test evaluates theperformance of positive and negative relevance feedback, which can be generated automatically from the relevance judgments. To illustrate our purpose, a freely available, non-copyright image collection is used to evaluate our CBIRS, Viper. All scripts described here are also freely available for download.
Keywords Image retrievalContent based retrievalImage databasesComputer visionInformation retrievalRelational databasesMultimedia databasesVideosMultimedia systemsBenchmark testing
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Research groups Computer Vision and Multimedia Laboratory
Viper group
Multimodal Interaction Group
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MULLER, Henning et al. Automated Benchmarking in Content-based Image Retrieval. In: Proceedings of the 2001 IEEE International Conference on Multimedia and Expo, ICME2001. Tokyo (Japan). [s.l.] : [s.n.], 2001. https://archive-ouverte.unige.ch/unige:47851

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Deposited on : 2015-03-06

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