Subject: Automatic Image Annotation, Scale Invariant Feature Transform, Edge Histogram Descriptor, Hierarchical Multi-Label Classification, Predictive Clustering Trees, Random Forests
Year: 2010
Type: Journal Article
Title: ImageCLEF 2009 medical image annotation task: PCTs for hierarchical multi-label classification
Author: Dimitrovski, Ivica
Author: Kocev, Dragi
Author: Loshkovska, Suzana
Author: Djeroski, Sasho
Abstract: In this paper, we describe an approach for the automatic medical image annotation task of the 2009 CLEF cross-language image retrieval campaign (ImageCLEF). This work is focused on the process of feature extraction from radiological images and hierarchical multi-label classification. To extract features from the images we used an edge histogram descriptor as global feature and SIFT histogram as local feature. These feature vectors were combined through simple concatenation in one feature vector with 2080 variables. With the combination of global and local features we want to tackle the problem of intra-class variability vs. inter-class similarity and the problem of data unbalance between train and test datasets. For classification we selected an extension of the predictive clustering trees (PCTs) able to handle data types organized in hierarchy. Furthermore, we constructed ensembles (Bagging and Random Forests) that use PCTs as base classifiers to improve the performance.
Publisher: Springer Berlin/Heidelberg
Relation: Multilingual Information Access Evaluation II. Multimedia Experiments
Identifier: oai:repository.ukim.mk:20.500.12188/23161
Identifier: http://hdl.handle.net/20.500.12188/23161
