Metadata-based similarity measurement is far from obsolete in our days, despite research’s focus on content and context. It allows for aggregating information from textual references, measuring similarity when content is not available, traditional keyword search in search engines, merging results in meta-search engines and many more research and industry interesting activities. Existing similarity measures do not take into consideration neither the unique nature of multimedia’s metadata nor the requirements of metadata-based information retrieval of multimedia. This work proposes a customised for the commonly available author-title multimedia metadata hybrid similarity measure that is shown through experimentation to be significantly more effective than baseline measures.
Karydis, Ioannis; Kanavos, Andreas; Sioutas, Spyros; Avlonitis, Markos; and Karacapilidis, Nikos, "LESIM: A Novel Lexical Similarity Measure Technique for Multimedia Information Retrieval" (2018). MCIS 2018 Proceedings. 31.