Abstract
In this paper we consider the task of categorising images of the Corel collection into semantic classes. In our earlier work, we demonstrated that state-of-the-art accuracy of supervised categorising of these images could be improved significantly by fusion of a large number of global image features. In this work, we preserve the general framework, but improve the components of the system: we modify the set of image features to include interest point histogram features, perform elementary feature classification with support vector machines (SVM) instead of self-organising map (SOM) based classifiers, and fuse the classification results with either an additive, multiplicative or SVM-based technique. As the main result of this paper, we are able to achieve a significant improvement of image categorisation accuracy by applying these generic state-of-the-art image content analysis techniques.
Supported by the Academy of Finland in the projects Neural methods in information retrieval based on automatic content analysis and relevance feedback and Finnish Centre of Excellence in Adaptive Informatics Research. Special thanks to Xiaojun Qi and Yutao Han for helping with the experimental setup.
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Viitaniemi, V., Laaksonen, J. (2007). Improving the Accuracy of Global Feature Fusion Based Image Categorisation. In: Falcidieno, B., Spagnuolo, M., Avrithis, Y., Kompatsiaris, I., Buitelaar, P. (eds) Semantic Multimedia. SAMT 2007. Lecture Notes in Computer Science, vol 4816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77051-0_1
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DOI: https://doi.org/10.1007/978-3-540-77051-0_1
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