Abstract
Image classification is usually accomplished using primitive features such as colour, shape and texture as feature vectors. Such vector model based classification has one large defect: it only deals with numerical features without considering the structural information within each image (e.g. attributes of objects, and relations between objects within one image). By including this sort of structural information, it is suggested that image classification accuracy can be improved. In this paper we introduce a framework for graph-based image classification using a weighting scheme. The schema was tested on a synthesized image dataset using different classification techniques. The experiments show that the proposed framework gives significantly better results than graph-based image classification in which no weighting is imposed.
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Jiang, C., Coenen, F. (2009). Graph-based Image Classification by Weighting Scheme. In: Allen, T., Ellis, R., Petridis, M. (eds) Applications and Innovations in Intelligent Systems XVI. SGAI 2008. Springer, London. https://doi.org/10.1007/978-1-84882-215-3_5
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DOI: https://doi.org/10.1007/978-1-84882-215-3_5
Publisher Name: Springer, London
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