ImageCLEF 2009 medical image annotation task: PCTs for hierarchical multi-label classification
Journal
Multilingual Information Access Evaluation II. Multimedia Experiments
Date Issued
2010
Author(s)
Kocev, Dragi
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.
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.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
dimitrovski-paperclef2009-with-cover-page-v2.pdf
Size
467.33 KB
Format
Adobe PDF
Checksum
(MD5):127f79ae1c02f1c642e611e6785519ec