Abstract
We propose a new image classification scheme based on the idea of mining jumping emerging substrings between classes of images represented by visual features. Jumping emerging substrings (JES) are string patterns, which occur frequently in one set of string data and are absent in another. By representing images in symbolic manner, according to their color and texture characteristics, we enable mining of JESs in sets of visual data and use mined patterns to create efficient and accurate classifiers. In this paper we describe our approach to image representation and provide experimental results of JES-based classification of well-known image datasets.
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Kobyliński, Ł., Walczak, K. (2009). Jumping Emerging Substrings in Image Classification. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_89
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DOI: https://doi.org/10.1007/978-3-642-03767-2_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03766-5
Online ISBN: 978-3-642-03767-2
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