A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Discriminative Training for Object Recognition Using Image Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Discriminative cue integration for medical image annotation
Pattern Recognition Letters
Decision trees for hierarchical multi-label classification
Machine Learning
Ensembles of Multi-Objective Decision Trees
ECML '07 Proceedings of the 18th European conference on Machine Learning
Analysis of time series data with predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Overview of the CLEF 2009 medical image annotation track
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Superpixel-Based interest points for effective bags of visual words medical image retrieval
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Evaluation of fast 2d and 3d medical image retrieval approaches based on image miniatures
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
A genetic algorithm for Hierarchical Multi-Label Classification
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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In this paper, we describe an approach to the automatic medical image annotation task of the 2009 CLEF cross-language image retrieval campaign (ImageCLEF). This work focuses on the process of feature extraction from radiological images and their hierarchical multi-label classification. To extract features from the images we use two different techniques: edge histogram descriptor (EHD) and Scale Invariant Feature Transform (SIFT) histogram. To annotate the images, we use predictive clustering trees (PCTs) which are able to handle target concepts that are organized in a hierarchy, i.e., perform hierarchical multi-label classification. Furthermore, we construct ensembles (Bagging and Random Forests) that use PCTs as base classifiers: this improves the predictive/ classification performance.