Abstract
The interpretation of natural scenes, generally so obvious and effortless for humans, still remains a challenge in computer vision. We propose in this article to design binary classifiers capable to recognise some generic image categories. Images are represented by graphs of regions and we define a graph edit distance to measure the dissimilarity between them. Furthermore a feature selection step is used to pick in the image the most meaningful regions for a given category and thus have a compact and appropriate graph representation.
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Le Saux, B., Bunke, H. (2005). Feature Selection for Graph-Based Image Classifiers. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_19
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DOI: https://doi.org/10.1007/11492542_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26154-4
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