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
Ontology driven content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Semantics and CBIR: a medical imaging perspective
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Psychophysical evaluation for a qualitative semantic image categorisation and retrieval approach
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Semantics extraction from images
Knowledge-driven multimedia information extraction and ontology evolution
Semantic hierarchies for image annotation: A survey
Pattern Recognition
Spatial role labeling: Towards extraction of spatial relations from natural language
ACM Transactions on Speech and Language Processing (TSLP)
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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 spatial context is subsequently exploited. Specifically, fuzzy spatial relations along with the previously computed initial annotations are supplied to a genetic algorithm, which uses them to decide on the globally most plausible annotation. In this work, two different fitness functions for the GA are tested and evaluated. Experiments with outdoor photographs demonstrate the performance of the proposed approaches.