An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local versus Global Features for Content-Based Image Retrieval
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative cue integration for medical image annotation
Pattern Recognition Letters
False positive reduction in mammographic mass detection using local binary patterns
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Overview of the ImageCLEFmed 2006 medical retrieval and medical annotation tasks
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Towards the improvement of textual anatomy image classification using image local features
MMAR '11 Proceedings of the 2011 international ACM workshop on Medical multimedia analysis and retrieval
| Hi-index | 0.00 |
This paper presents the algorithms and results of the "idiap" team participation to the ImageCLEFmed annotation task in 2008. On the basis of our successful experience in 2007 we decided to integrate two different local structural and textural descriptors. Cues are combined through concatenation of feature vectors and through the Multi-Cue Kernel. The challenge this year was to annotate images coming mainly from classes with only few training examples. We tackled the problem on two fronts: (1) we introduced a further integration strategy using SVM as an opinion maker; (2) we enriched the poorly populated classes adding virtual examples. We submitted several runs considering different combinations of the proposed techniques. The run jointly using the feature concatenation, the confidence-based opinion fusion and the virtual examples ranked first among all submissions.