Fundamentals of digital image processing
Fundamentals of digital image processing
A novel relevance feedback technique in image retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Data object and label placement for information abundant visualizations
Proceedings of the 1998 workshop on New paradigms in information visualization and manipulation
A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Direct Annotation: A Drag-and-Drop Strategy for Labeling Photos
IV '00 Proceedings of the International Conference on Information Visualisation
Vidya: an experiential annotation system
ETP '03 Proceedings of the 2003 ACM SIGMM workshop on Experiential telepresence
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Interfaces for networked media exploration and collaborative annotation
Proceedings of the 10th international conference on Intelligent user interfaces
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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In this paper, we present a novel image annotation approach with an emphasis on – (a) common sense based semantic propagation, (b) visual annotation interfaces and (c) novel evaluation schemes. The annotation system is interactive, intuitive and real-time. We attempt to propagate semantics of the annotations, by using WordNet and ConceptNet, and low-level features extracted from the images. We introduce novel semantic dissimilarity measures, and propagation frameworks. We develop a novel visual annotation interface that allows a user to group images by creating visual concepts using direct manipulation metaphors without manual annotation. We also develop a new evaluation technique for annotation that is based on relationship between concepts based on commonsensical relationships. Our Experimental results on three different datasets, indicate that the annotation system performs very well. The semantic propagation results are good – we converge close to the semantics of the image by annotating a small number (~16.8%) of database images.