Abstract
This paper deals with knowledge extraction from visual data for content-based image retrieval of natural scenes. Images are analysed using a ridgelet transform that enhances information at different scales, orientations and spatial localizations. The main contribution of this work is to propose a method that reduces the size and the redundancy of this ridgelet representation, by defining both global and local signatures that are specifically designed for semantic classification and content-based retrieval. An effective recognition system can be built when these descriptors are used in conjunction with a support vector machine (SVM). Classification and retrieval experiments are conducted on natural scenes, to demonstrate the effectiveness of the approach.
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Le Borgne, H., O’Connor, N. (2005). Natural Scene Classification and Retrieval Using Ridgelet-Based Image Signatures. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_15
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DOI: https://doi.org/10.1007/11558484_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
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