Detection of Visual Concepts and Annotation of Images using Predictive Clustering Trees
Journal
Working Notes of CLEF
Date Issued
2010
Author(s)
Kocev, Dragi
Djeroski, Sasho
Abstract
In this paper, we present a multiple targets classification system for visual concepts detection and image annotation. Multiple targets
classification (MTC) is a variant of classification where an instance may
belong to multiple classes at the same time. The system is composed
of two parts: feature extraction and classification/annotation. The feature extraction part provides global and local descriptions of the images.
These descriptions are then used to learn a classifier and to annotate an
image with the corresponding concepts. To this end, we use predictive
clustering trees (PCTs), which are capable to classify an instance to multiple classes at once, thus exploit the interactions that may occur among
the different visual concepts (classes). Moreover, we constructed ensembles (random forests) of PCTs, to improve the predictive performance.
We tested our system on the image database from the visual concept
detection and annotation task part of ImageCLEF 2010. The extensive
experiments conducted on the benchmark database show that our system has very high predictive performance and can be easily scaled to
large number of images and visual concepts.
classification (MTC) is a variant of classification where an instance may
belong to multiple classes at the same time. The system is composed
of two parts: feature extraction and classification/annotation. The feature extraction part provides global and local descriptions of the images.
These descriptions are then used to learn a classifier and to annotate an
image with the corresponding concepts. To this end, we use predictive
clustering trees (PCTs), which are capable to classify an instance to multiple classes at once, thus exploit the interactions that may occur among
the different visual concepts (classes). Moreover, we constructed ensembles (random forests) of PCTs, to improve the predictive performance.
We tested our system on the image database from the visual concept
detection and annotation task part of ImageCLEF 2010. The extensive
experiments conducted on the benchmark database show that our system has very high predictive performance and can be easily scaled to
large number of images and visual concepts.
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