Classification of Digital Photos Taken by Photographers or Home Users

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Tong, H., Li, M., Zhang, H., He, J., Zhang, C.: Classification of Digital Photos Taken by Photographers or Home Users. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM (1). LNCS, vol. 3332, pp. 198–205. Springer, Heidelberg (2004).



In this paper, we address a specific image classification task, i.e. to group images according to whether they were taken by photographers or home users. Firstly, a set of low-level features explicitly related to such high-level semantic concept are investigated together with a set of general-purpose low-level features. Next, two different schemes are proposed to find out those most discriminative features and feed them to suitable classifiers: one resorts to boosting to perform feature selection and classifier training simultaneously; the other makes use of the information of the label by Principle Component Analysis for feature re-extraction and feature de-correlation; followed by Maximum Marginal Diversity for feature selection and Bayesian classifier or Support Vector Machine for classification. In addition, we show an application in No-Reference holistic quality assessment as a natural extension of such image classification. Experimental results demonstrate the effectiveness of our methods.

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