Abstract
The image Euclidean distance (IMED) is a class of image metric that takes the spatial relationship between pixels into consideration. Sun et al. [9] showed that IMED is equivalent to a translation-invariant transform. In this paper, we extend the equivalency to the discrete frequency domain. Based on the connection, we show that GED and IMED can be implemented as low-pass filters, which reduce the space and time complexities significantly. The transform domain metric learning (TDML) proposed in [9] is also resembled as a translation-invariant counterpart of LDA. Experimental results demonstrate improvements in algorithm efficiency and performance boosts on the small sample size problems.
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Sun, B., Feng, J., Wang, G. (2013). The Translation-Invariant Metric and Its Application. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_59
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DOI: https://doi.org/10.1007/978-3-642-42057-3_59
Publisher Name: Springer, Berlin, Heidelberg
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