Abstract
This paper aims to introduce a new distance measure for images, called Local Patch Dissimilarity. This new distance measure is inspired from rank distance which is a distance measure for strings.
The distance measure introduced in this paper is based on patches. There are many other patch-based techniques used in image processing. Patches contain contextual information and have advantages in terms of generalization.
An algorithm that computes the Local Patch Dissimilarity between two images is presented in this work. Experiments show that the extension of rank distance to images has very good results in image classification, more precisely in handwritten digit recognition.
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Dinu, L.P., Ionescu, RT., Popescu, M. (2012). Local Patch Dissimilarity for Images. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_15
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DOI: https://doi.org/10.1007/978-3-642-34475-6_15
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