Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biomedical Imaging, Visualization, and Analysis
Biomedical Imaging, Visualization, and Analysis
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Discriminative cue integration for medical image annotation
Pattern Recognition Letters
Grayscale medical image annotation using local relational features
Pattern Recognition Letters
A Multiple Kernel Learning Approach to Joint Multi-class Object Detection
Proceedings of the 30th DAGM symposium on Pattern Recognition
Automated Flower Classification over a Large Number of Classes
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
A food image recognition system with multiple kernel learning
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
FIRE in ImageCLEF 2005: combining content-based image retrieval with textual information retrieval
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Medical image annotation and retrieval using visual features
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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Nowadays, medical images are generated by hospitals and medical centers rapidly. The large volume of medical image data produces a strong need to effective medical image retrieval. The visual characteristic of medical image, such as modality, anatomical region etc., are important information and can be used to improve the retrieval process. Even though some of the information is contained in the DICOM headers, it has been reported that DICOM headers contain a relatively high rate of errors. And for on-line medical collection, these metadata can be lost when medical images are compressed. In this paper, we propose an algorithm for medical image classification according to their visual content. Our method uses multiple kernel learning (MKL) to combine different visual features, and learn the optimal mixing weights for each class adaptively. This method is evaluated on a medical image dataset with 1400 images, and the experimental results demonstrate the effectiveness of our method.