Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Scalable discriminant feature selection for image retrieval and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Image classification could be treated as an effective solution to enable keyword-based semantic image retrieval, while feature selection is a key issue in categorization. In this paper, we propose a novel strategy by using feature selection in learning semantic concepts of image categories. To choose representative and informative features for an image category and meanwhile reduce noisy features, a feature selection strategy is proposed. In the feature selection stage, salient patches are first detected by SIFT descriptor and clustered by DENCLUE algorithm. Then the pointwise mutual information between the salient patches and the image category is calculated to evaluate the important patches and construct the visual vocabulary for the category. Based on the selected visual features, the SVM classifier is applied to categorization. The experimental results on Corel image database demonstrate that the proposed feature selection approach is very effective in image classification and visual concept learning.