Medical Image Retrieval: ISSR at CLEF 2009
نویسندگان
چکیده
This paper represents the first participation of the Institute of Statistical Studies and Research at Cairo University group in CLEF 2009-Medical image retrieval track. Our system uses Lemur toolkit for text retrieval. The main objective is to carry out retrieving medical image depending on associated image text. We experimented with different text features such as article title, image caption and the article paragraph(s) denoting to the image. We propose a simple and effective extraction method to find relevant paragraphs based on the structure of HTML files. Automatic translation of queries in different languages other than collection language is also experimented. In this paper the results of 9 runs are presented in order to compare retrieval based on different text features and the effect of stop word lists and the use of relevance feedback. Categories and subject descriptors H. Information Systems; H.3 Information Storage And Retrieval; H.3.1 Content Analysis and Indexing; H.3.4 Systems and Software;
منابع مشابه
Medical Image Retrieval: ISSR at CLEF 2010
This is the second participation of Institute of Statistical Studies and Research (ISSR) group in CLEF 2010-Medical image retrieval track. This paper describes our experiments in monolingual and multilingual tasks. First, we test Paragraph Extraction (PE) and Sentence Selection (SS) approaches on the classical medical retrieval task (Ad-hoc), as well as on Case-based retrieval. Second, we compa...
متن کاملUnited Institute of Informatics - Problems at CLEF 2009 Medical Image Retrieval Task
This paper describes methods and results archived by our research group at the Cross Language Evaluation Forum (CLEF’2009). In our work we concentrated on the medical image retrieval task. All the attention was given to the retrieval based on nine visual topics only. No textual information was considered. Our goal was to develop and comparatively assess image descriptors for content based retri...
متن کاملDocument Expansion for Text-Based Image Retrieval at CLEF 2009
In this paper, we describe and analyze our participation in the WikipediaMM task at CLEF 2009. Our main efforts concern the expansion of the image metadata from the Wikipedia abstracts collection DBpedia. In our experiments, we use the Okapi feedback algorithm for document expansion. Compared with our text retrieval baseline, our best document expansion RUN improves MAP by 17.89%. As one of our...
متن کاملUB at CLEF 2005: Bilingual CLIR and Medical Image Retrieval Tasks
This paper presents the results of the State University of New York at Buffalo in the Cross Language Evaluation Forum 2005 (CLEF 2005). We participated in monolingual Portuguese, bilingual EnglishPortuguese and in the medical image retrieval tasks. We used the SMART retrieval system for text retrieval in the mono and bilingual retrieval tasks on Portuguese documents. The main goal of this part ...
متن کاملUB at CLEF2004 Cross Language Medical Image Retrieval
This paper presents the results of the State University of New York at Buffalo in the cross-language medical image retrieval task at CLEF 2004. Our work in image retrieval explores the combination of image and text retrieval using automatic query expansion. The system uses pseudo relevance feedback on the case descriptions associated with the top 10 images to improve ranking of images retrieved...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009