Abstract
Similarity plays an important role in many multimedia retrieval applications. However, it often has many facets and its perception is highly subjective – very much depending on a person’s background or retrieval goal. In previous work, we have developed various approaches for modeling and learning individual distance measures as a weighted linear combination of multiple facets in different application scenarios. Based on a generalized view of these approaches as an optimization problem guided by generic relative distance constraints, we describe ways to address the problem of constraint violations and finally compare the different approaches against each other. To this end, a comprehensive experiment using the Magnatagatune benchmark dataset is conducted.
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Stober, S., Nürnberger, A. (2013). An Experimental Comparison of Similarity Adaptation Approaches. In: Detyniecki, M., García-Serrano, A., Nürnberger, A., Stober, S. (eds) Adaptive Multimedia Retrieval. Large-Scale Multimedia Retrieval and Evaluation. AMR 2011. Lecture Notes in Computer Science, vol 7836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37425-8_8
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DOI: https://doi.org/10.1007/978-3-642-37425-8_8
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