Content Based Image Retrieval Using HDMR Constant Term Based Clustering
Springer Proceedings in Mathematics and Statistics, ISSN: 2194-1017, Vol: 384, Page: 35-45
2022
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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.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
The studies related with the content-based image retrieval (CBIR) has increased because of both necessity for efficient image retrieval and the limitations in large-scale systems. Efficient image retrieval refers to finding accurate image from the database with high speed. This paper presents a new efficient image retrieval method using High Dimensional Model Representation (HDMR). The method has two main steps, clustering and retrieval. In clustering part, we use k-means method on HDMR constant term while in the subsequent part, we retrieve the most similar images to a given query image from a relevant cluster. We experiment the efficiency and effectiveness of the new algorithm on Columbia Object Image Library (COIL-100) and get conspicuous results. These results are tabulated in the paper.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128982265&origin=inward; http://dx.doi.org/10.1007/978-3-030-96401-6_3; https://link.springer.com/10.1007/978-3-030-96401-6_3; https://dx.doi.org/10.1007/978-3-030-96401-6_3; https://link.springer.com/chapter/10.1007/978-3-030-96401-6_3
Springer Science and Business Media LLC
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