Color quantization by dynamic programming and principal analysis
ACM Transactions on Graphics (TOG)
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Confidence-based dynamic ensemble for image annotation and semantics discovery
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Modeling, classifying and annotating weakly annotated images using Bayesian network
Journal of Visual Communication and Image Representation
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Extracting high-level semantic concepts from low-level visual features of images is a very challenging research. Although traditional machine learning approaches just extract fragmentary information of images, their performance is still not satisfying. In this paper, we propose a novel system that automatically extracts high-level concepts such as spatial relationships or natural-enemy relationships from images using combination of ontologies and SVM classifiers. Our system consists of two phases. In the first phase, visual features are mapped to intermediate-level concepts (e.g, yellow, 45 angular stripes). And then, a set of these concepts are classified into relevant object concepts (e.g, tiger) by using SVM-classifiers. In this phase, revision module which improves the accuracy of classification is used. In the second phase, based on extracted visual information and domain ontology, we deduce semantic relationships such as spatial/natural-enemy relationships between multiple objects in an image. Finally, we evaluate the proposed system using color images including about 20 object concepts.