Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Intelligent Information Systems
Automatic image annotation and retrieval using cross-media relevance models
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A picture is worth a thousand keywords: image-based object search on a mobile platform
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Latent semantic fusion model for image retrieval and annotation
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ACM Computing Surveys (CSUR)
Overview of the ImageCLEFmed 2008 medical image retrieval task
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Bridging the Gap: Query by Semantic Example
IEEE Transactions on Multimedia
NMF-based multimodal image indexing for querying by visual example
Proceedings of the ACM International Conference on Image and Video Retrieval
Semantic combination of textual and visual information in multimedia retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Efficient graffiti image retrieval
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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In this paper we propose a strategy to fuse visual features and unstructured-text data in a medical image retrieval system. The main goal of this work is to investigate whether the semantic information from text descriptions can be transfered to a visual similarity measure. Then, a system to search using the query-by-example paradigm is evaluated instead of a keyword-based search. We achieve this by using Latent Semantic Kernels to generate a new representation space whose coordinates define latent concepts that merge visual patterns and textual terms. The proposed method is tested in a medical image collection from the ImageCLEFmed08 challenge. The experimental evaluation tests the system using different image queries. The results show an improvement of the visual-text fused approach with respect to only using visual information.