Distance measures for signal processing and pattern recognition
Signal Processing
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
FeedbackBypass: A New Approach to Interactive Similarity Query Processing
Proceedings of the 27th International Conference on Very Large Data Bases
A Sound Algorithm for Region-Based Image Retrieval Using an Index
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling link-based similarity search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Context-Based image similarity queries
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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The advent of digital photography calls for effective techniques for managing growing amounts of color images. Systems that only rely on low-level image features are nowadays limited by the semantic gap problem, which leads to a mismatch between the user subjective notion of similarity and the one adopted by a system. A possible way to reduce the semantic gap is to (semi-)automatically assign meaningful terms to images, so as to enable a high-level, concept-based, retrieval. In this paper we explore the opportunities offered by graph-based link analysis techniques in the development of a semi-automatic image captioning system. The approach we propose is appealing since the predicted terms for an image are in variable number, depending on the image content, represent correlated terms, and can also describe abstract concepts. We present preliminary results on our prototype system and discuss possible extensions.