Visual information retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles of visual information retrieval
Principles of visual information retrieval
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
An Optimized Interaction Strategy for Bayesian Relevance Feedback
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The Journal of Machine Learning Research
A Probabilistic Active Support Vector Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Retrieval of difficult image classes using svd-based relevance feedback
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Region-based image retrieval using an object ontology and relevance feedback
EURASIP Journal on Applied Signal Processing
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Intention-focused active reranking for image object retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
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Many of the available image databases have keyword annotations associated with the images. In spite of the availability of good quality low-level visual features that reflect well the physical content, image retrieval based on visual features alone is subject to semantic gap. Text annotations are related to image context or semantic interpretation of the visual content and are not necessarely directly linked to the visual appearance of the images. Keywords and visual features thus provide complementary information. Using both sources of information is an advantage in many applications and recent work in this area reflects this interest. In this paper, we address the challenge of semantic gap reduction using a hybrid visual and conceptual representation of the content within an active relevance feedback context. We introduce a new feature vector, based on the keyword annotations available for the images, which makes use of conceptual information extracted from an external lexical database, information represented by a set of "core concepts". Our experiments show that the use of the proposed hybrid conceptual and visual feature vector dramatically improves the quality of the relevance feedback results.