TY - JOUR
T1 - Classification of digital photos taken by photographers or home users
AU - Tong, Hanghang
AU - Li, Mingjing
AU - Zhang, Hong Jiang
AU - He, Jingrui
AU - Zhang, Changshui
N1 - Funding Information:
or ’by home user’. A set of low-level features which are explicitly related to such specific high level semantic concept are investigated together with a set of general-purpose low-level features. To find out those most discriminative features and feed them to suitable classifiers, we propose two different schemes: one is boosting based, in which situation we make use of the cherished properties of boosting methods to perform feature selection and classifier training simultaneously; the other is feature re-extraction based, in which context we resort to PCA in a supervised manner to re-extract some more discriminative features from the initial weak features; then we use MMD to select those most discriminative ones and feed them to SVM or Bayesian classifier. Moreover, de-correlation on different dimensions of features by PCA also makes the subsequent feature selection step more reliable. While the first scheme is very simple, the latter one is more sophisticated and produces higher performance for our problem. As a natural extension, we show an application of such image classification in No-Reference holistic quality assessment. Experimental results on 29540 digital images and Acknowledgements. This work was supported by National High Technology Research and Development Program of China (863 Program) under contract No.2001AA114190.
PY - 2004
Y1 - 2004
N2 - 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 reextraction 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.
AB - 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 reextraction 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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U2 - 10.1007/978-3-540-30541-5_25
DO - 10.1007/978-3-540-30541-5_25
M3 - Article
AN - SCOPUS:35048832373
SN - 0302-9743
VL - 3331
SP - 198
EP - 205
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -