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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Constraint Violation
- Distance Constraint
- Similarity Judgment
- Weighted Linear Combination
- Similarity Adaptation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bade, K.: Personalized Hierarchical Structuring. PhD thesis, Otto-von-Guericke-University Magdeburg (2009)
Bade, K., Garbers, J., Stober, S., Wiering, F., Nürnberger, A.: Supporting folk-song research by automatic metric learning and ranking. In: Proc. of the 10th Int. Conf. on Music Information Retrieval (ISMIR 2009) (2009)
Bade, K., Nürnberger, A.: Creating a cluster hierarchy under constraints of a partially known hierarchy. In: Proc. of the 2008 SIAM Int. Conf. on Data Mining (2008)
Cheng, W., Hüllermeier, E.: Learning similarity functions from qualitative feedback. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 120–134. Springer, Heidelberg (2008)
Donaldson, J., Lamere, P.: Using visualizations for music discovery. In: Tutorial at the 10th Int. Conf. on Music Information Retrieval (ISMIR 2009) (2009)
Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: Liblinear: A library for large linear classification. The Journal of Machine Learning Research 9, 1871–1874 (2008)
Goldfarb, D., Idnani, A.: A numerically stable dual method for solving strictly convex quadratic programs. Mathematical Programming 27(1), 1–33 (1983)
Joachims, T.: A Support Vector Method for Multivariate Performance Measures. In: Proc. of the Int. Conf. on Machine Learning (ICML 2005) (2005)
Law, E., von Ahn, L.: Input-agreement: a new mechanism for collecting data using human computation games. In: Proc. of the 27th Int. Conf. on Human Factors in Computing Systems (CHI 2009) (2009)
Lübbers, D., Jarke, M.: Adaptive multimodal exploration of music collections. In: Proc. of the 10th Int. Conf. on Music Information Retrieval (ISMIR 2009) (2009)
McFee, B., Barrington, L., Lanckriet, G.: Learning similarity from collaborative filters. In: Proc. of the 11th Int. Conf. on Music Information Retrieval (ISMIR 2010) (2010)
McFee, B., Lanckriet, G.: Heterogeneous embedding for subjective artist similarity. In: Proc. of the 10th Int. Conf. on Music Information Retrieval (ISMIR 2009) (2009)
Nürnberger, A., Klose, A.: Improving clustering and visualization of multimedia data using interactive user feedback. In: Proc. of the 9th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2002) (2002)
Salton, G., Buckley, C.: Term weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988)
Slaney, M., Weinberger, K.Q., White, W.: Learning a metric for music similarity. In: Proc. of the 9th Int. Conf. on Music Information Retrieval (ISMIR 2008) (2008)
Nürnberger, A., Stober, S.: User modelling for interactive user-adaptive collection structuring. In: Boujemaa, N., Detyniecki, M., Nürnberger, A. (eds.) AMR 2007. LNCS, vol. 4918, pp. 95–108. Springer, Heidelberg (2008)
Stober, S., Nürnberger, A.: Towards user-adaptive structuring and organization of music collections. In: Detyniecki, M., Leiner, U., Nürnberger, A. (eds.) AMR 2008. LNCS, vol. 5811, pp. 53–65. Springer, Heidelberg (2010)
Stober, S., Nürnberger, A.: Similarity adaptation in an exploratory retrieval scenario. In: Detyniecki, M., Knees, P., Nürnberger, A., Schedl, M., Stober, S. (eds.) AMR 2010. LNCS, vol. 6817, pp. 144–158. Springer, Heidelberg (2012)
Stober, S.: Adaptive distance measures for exploration and structuring of music collections. In: Proc. of AES 42nd Conf. on Semantic Audio (2011)
Wolff, D., Weyde, T.: Combining Sources of Description for Approximating Music Similarity Ratings. In: Detyniecki, M., García-Serrano, A., Nürnberger, A., Stober, S. (eds.) AMR 2011. LNCS, vol. 7836, pp. 114–124. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-37425-8_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37424-1
Online ISBN: 978-3-642-37425-8
eBook Packages: Computer ScienceComputer Science (R0)