Detecting Changes in Crowdsourced Social Media Images
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14420 LNCS, Page: 195-211
2023
- 1Citations
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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.
Metrics Details
- Citations1
- Citation Indexes1
Conference Paper Description
We propose a novel service framework to detect changes in crowdsourced images. We use a service-oriented approach to model and represent crowdsourced images as image services. Non-functional attributes of an image service are leveraged to detect changes in an image. The changes are reported in form of a version tree. The version tree is constructed in a way that it reflects the extent of changes introduced in different versions. Afterwards, we find semantic differences in between different versions to determine the extent of changes introduced in a specific version. Preliminary experimental results demonstrate the effectiveness of the proposed approach.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85178567769&origin=inward; http://dx.doi.org/10.1007/978-3-031-48424-7_15; https://link.springer.com/10.1007/978-3-031-48424-7_15; https://dx.doi.org/10.1007/978-3-031-48424-7_15; https://link.springer.com/chapter/10.1007/978-3-031-48424-7_15
Springer Science and Business Media LLC
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