Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Overview of the ImageCLEFmed 2007 Medical Retrieval and Medical Annotation Tasks
Advances in Multilingual and Multimodal Information Retrieval
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
Search result re-ranking by feedback control adjustment for time-sensitive query
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Overview of the CLEF 2009 medical image retrieval track
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Correlating medical-dependent query features with image retrieval models using association rules
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this paper, we present an empirical study for monolingual medical image retrieval. In particular, we present a series of experiments in ImageCLEFmed 2009 task. There are three main goals. First, we evaluate traditional well-known weighting models in the text retrieval domain, such as BM25, TFIDF and Language Model (LM), for context-based image retrieval. Second, we evaluate statistical-based feedback models and ontology-based feedback models. Third, we investigate how content-based image retrieval can be integrated with these two basic technologies in traditional text retrieval domain. The experimental results have shown that: 1) traditional weighting models work well in context-based medical image retrieval task especially when the parameters are tuned properly; 2) statistical-based feedback models can further improve the retrieval performance when a small number of documents are used for feedback; however, the medical image retrieval can not benefit from ontology-based query expansion method used in this paper; 3) the retrieval performance can be slightly boosted via an integrated retrieval approach.