Abstract
This paper proposes a similarity ranking technique that exploits the entire network structure of similarity relationships for multimedia, particularly image, databases. The main problem in the similarity ranking on multimedia is the meaning gap between the characteristics automatically computed from the multimedia dataset and the interpretation by human from the multimedia itself. In fact, the similarity semantics usually lies on high level human interpretation and automatically computed low level multimedia properties may not reflect it. This paper assumes that the meaning of the multimedia is affected by the context or similarity relationships in a dataset and therefore, we propose the ranking technique to catch the semantics from a large multimedia dataset. This similarity ranking technique based on the context or similarity relationships yields better experimental results than the conventional similarity ranking techniques.
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Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03036561).
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Cha, GH. Similarity ranking technique exploiting the structure of similarity relationships. Computing 105, 559–576 (2023). https://doi.org/10.1007/s00607-020-00859-w
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DOI: https://doi.org/10.1007/s00607-020-00859-w
Keywords
- Multimedia retrieval
- Image retrieval
- Semantic learning
- Similarity search
- Similarity relationship
- Similarity ranking
- Context relationship