Abstract
This paper aims to develop a theoretical framework for learning pseudo metric (LPM) for multimedia data classification and retrieval. Neural networks are employed to approximate the LPM through learning feature examples. Training samples are generated by a k-MEAN clustering technique, and practical criteria for verifying the approximation performance are presented. The LPM metric is evaluated using a semantic image classification task where the database contains 11 categories of natural images with an accurate ground truth. Experimental results demonstrate the usefulness and effectiveness of the proposed techniques for multimedia data classification and retrieval.
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© 2004 Springer-Verlag Berlin Heidelberg
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Wang, D., Ma, X. (2004). Learning Pseudo Metric for Multimedia Data Classification and Retrieval. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_142
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DOI: https://doi.org/10.1007/978-3-540-30132-5_142
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
Online ISBN: 978-3-540-30132-5
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