A Comparative Evaluation of Retrieval Methods for Duplicate Search in Image Database
Journal of Visual Languages & Computing, ISSN: 1045-926X, Vol: 12, Issue: 1, Page: 105-120
2001
- 12Citations
- 1Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Visual database systems are continuously enriched by original and processed images derived from the original using editing techniques. This last type of image (called duplicate) represents a relevant quote of unnecessary stored images and, moreover a clear case of image similarity. In this paper a comparative evaluation between image retrieval methods in the specific case of duplicate search is proposed. All methods are based on low-level features extraction, such as colors, shape and patterns, and are suitable in automatic systems. The performance comparison is made in terms of effectiveness using a database of 6368 natural pictures and 12 test sets of 18 images each.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1045926X99901544; http://dx.doi.org/10.1006/jvlc.1999.0154; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=0347139217&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1045926X99901544; https://api.elsevier.com/content/article/PII:S1045926X99901544?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S1045926X99901544?httpAccept=text/plain; https://dx.doi.org/10.1006/jvlc.1999.0154
Elsevier BV
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