Detection of Visual Concepts and Annotation of Images Using Predictive Clustering Trees

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...

• Type: Conference
• Year: 2010
• Where: CLEF
• Authors: Ivica Dimitrovski, Dragi Kocev, Suzana Loskovska, Saso Dzeroski
• Keywords: CLEF 2010, Feature Extraction, Information Technology, Multiple Targets Classification, Visual Concepts