Saliency, Scale and Image Description
International Journal of Computer Vision
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Toward bridging the annotation-retrieval gap in image search by a generative modeling approach
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
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
An efficient color representation for image retrieval
IEEE Transactions on Image Processing
Automatic image orientation detection
IEEE Transactions on Image Processing
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Image categorization could be treated as an effective solution to enable keyword-based image retrieval. In this paper, we propose a novel image categorization approach by learning semantic concepts of image categories. In order to choose representative features and meanwhile reduce noisy features, a three-step feature selection strategy is proposed. First, salient patches are detected. Then all the detected salient patches are clustered and the visual keyword vocabulary is constructed. Finally, the region of dominance and the salient entropy measure are calculated to reduce the similar and non-common noises of salient patches. Based on the selected visual keywords, the Integrated Patch (IP) model is proposed to describe and categorize images. As a generative model, the IP model represents the appearance of the combination of the visual keywords, considering the diversity of the object or the scene. The parameters are estimated by the EM algorithm. The experimental results on the Corel image dataset demonstrate that the proposed feature selection and the image description model are effective in image categorization.