An introduction to genetic algorithms
An introduction to genetic algorithms
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook on Ontologies (International Handbooks on Information Systems)
Handbook on Ontologies (International Handbooks on Information Systems)
Evaluating the application of semantic inferencing rules to image annotation
Proceedings of the 3rd international conference on Knowledge capture
Computing and Managing Cardinal Direction Relations
IEEE Transactions on Knowledge and Data Engineering
Semantic annotation of images and videos for multimedia analysis
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Knowledge-assisted semantic video object detection
IEEE Transactions on Circuits and Systems for Video Technology
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Combining global and local information for knowledge-assisted image analysis and classification
EURASIP Journal on Advances in Signal Processing
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In this paper, a learning approach coupling Support Vector Machines (SVMs) and a Genetic Algorithm (GA) is presented for knowledge-assisted semantic image analysis in specific domains. Explicitly defined domain knowledge under the proposed approach includes objects of the domain of interest and their spatial relations. SVMs are employed using low-level features to extract implicit information for each object of interest via training in order to provide an initial annotation of the image regions based solely on visual features. To account for the inherent visual information ambiguity, fuzzy spatial relations along with the previously computed initial annotations are supplied to a genetic algorithm, which decides on the globally most plausible annotation. Experiments with images of the beach vacation domain demonstrate the performance of the proposed approach.