Cross-Media Feedback Strategies: Merging Text and Image Information to Improve Image Retrieval

Autor:	Patrick Hède, Christian Fluhr, Romaric Besançon, Pierre-Alain Moëllic
Rok vydání:	2005
Předmět:	Automatic image annotation Information retrieval Computer science Human–computer information retrieval InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Cross media Visual Word Image retrieval Text retrieval Task (project management) Image (mathematics)
Zdroj:	Multilingual Information Access for Text, Speech and Images ISBN: 9783540274209 CLEF
DOI:	10.1007/11519645_69
Popis:	The CEA-LIST/LIC2M develops both cross-language information retrieval systems and content-based image retrieval systems. The ad hoc and medical tasks of the ImageCLEF campaign offered us the opportunity to perform some experiments on merging the results of the two systems. The results obtained show that the performance of each system highly depends on the corpus and the task: feedback strategies can improve the results, but the parameters used are to be tuned according to the confidence of each system on the task and corpus: for the ad hoc task, text retrieval performs good whereas results of image retrieval are poor. On the other hand, for the medical task, the image retrieval performs better, and text retrieval can improve overall results only with reinforcement strategies.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::517d8204181ed149053b755fdce603f9 https://doi.org/10.1007/11519645_69 Zobrazit plný text záznamu