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2020 | OriginalPaper | Buchkapitel

Logo Detection Based on FCM Clustering Algorithm and Texture Features

verfasst von : Wala Zaaboub, Lotfi Tlig, Mounir Sayadi, Basel Solaiman

Erschienen in: Image and Signal Processing

Verlag: Springer International Publishing

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Abstract

Logo detection methods usually depend on logo shapes and need for training data or a-priori information on the processed images. This limits their effectiveness to real-world applications. In this paper, we tackle these challenges by exploring the textural information. Specifically we propose a novel approach for administrative logo detection based on a fuzzy classification with a multi-fractal texture feature, capable of automatically characterizing texture measures describing logo and non-logo regions. Experimental results, using two real datasets, confirm the feasibility of the proposed method for degraded administrative documents. Extensive comparative evaluations demonstrate the superiority of this approach over the state-of-the-art methods.

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Titel: Logo Detection Based on FCM Clustering Algorithm and Texture Features
verfasst von: Wala Zaaboub
Lotfi Tlig
Mounir Sayadi
Basel Solaiman
Verlag: Springer International Publishing
Buch: Image and Signal Processing
Print ISBN: 978-3-030-51934-6

Electronic ISBN: 978-3-030-51935-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-51935-3_35

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