Abstract
This paper shows how strings can be used in a natural images classification task. We propose to build an attributed string from a set of regions of interest detected thanks to an interest point detector. These salient zones are characterized by local signatures describing singularities and they are linked by using graph seriation algorithms and perceptual methods. Once each image is represented by a string of signatures, we propose to use string-based edit distances and an ordered histograms-based distance in order to perform the classification task. Experiments have shown that whereas seriation algorithms give approximately the same results, the ordered histogram based distance is more efficient for the considered application.
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Ros, J., Laurent, C., Jolion, JM., Simand, I. (2005). Comparing String Representations and Distances in a Natural Images Classification Task. In: Brun, L., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2005. Lecture Notes in Computer Science, vol 3434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31988-7_8
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DOI: https://doi.org/10.1007/978-3-540-31988-7_8
Publisher Name: Springer, Berlin, Heidelberg
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