Language Translation and Media Transformation in Cross-Language Image Retrieval

We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

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Disambiguation of single noun translations extracted from bilingual comparable corpora

Terminology ◽

10.1075/term.7.1.06nak ◽

2001 ◽

Vol 7 (1) ◽

pp. 63-83 ◽

Cited By ~ 4

Author(s):

Hiroshi Nakagawa

Keyword(s):

Information Retrieval ◽

Language Translation ◽

Target Language ◽

Comparable Corpora ◽

Source Language ◽

Bilingual Dictionary ◽

Second Stage ◽

Cross Language Information Retrieval ◽

Machine Readable ◽

Cross Language

Bilingual machine readable dictionaries are important and indispensable resources of information for cross-language information retrieval, and machine translation. Recently, these cross-language informational activities have begun to focus on specific academic or technological domains. In this paper, we describe a bilingual dictionary acquisition system which extracts translations from non-parallel but comparable corpora of a specific academic domain and disambiguates the extracted translations. The proposed method is two-fold. At the first stage, candidate terms are extracted from a Japanese and English corpus, respectively, and ranked according to their importance as terms. At the second stage, ambiguous translations are resolved by selecting the target language translation which is the nearest in rank to the source language term. Finally, we evaluate the proposed method in an experiment.

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Content-based Image Retrieval for Scientific Literature Access

Methods of Information in Medicine ◽

10.3414/me0561 ◽

2009 ◽

Vol 48 (04) ◽

pp. 371-380 ◽

Cited By ~ 11

Author(s):

S. Antani ◽

Rodney Long ◽

T. M. Deserno

Keyword(s):

Image Retrieval ◽

Quantitative Estimate ◽

Literature Search ◽

Medical Literature ◽

Scientific Literature ◽

High Impact ◽

Content Based Image Retrieval ◽

Medical Applications ◽

Cross Language ◽

Search And Retrieval

Summary Objectives: An increasing number of articles are published electronically in the scientific literature, but access is limited to alphanumerical search on title, author, or abstract, and may disregard numerous figures. In this paper, we estimate the benefits of using content-based image retrieval (CBIR) on article figures to augment traditional access to articles. Methods: We selected four high-impact journals from the Journal Citations Report (JCR) 2005. Figures were automatically extracted from the PDF article files, and manually classified on their content and number of sub-figure panels. We make a quantitative estimate by projecting from data from the Cross-Language Evaluation Forum (Image-CLEF) campaigns, and qualitatively validate it through experiments using the Image Retrieval in Medical Applications (IRMA) project. Results: Based on 2077 articles with 11,753 pages, 4493 figures, and 11,238 individual images, the predicted accuracy for article retrieval may reach 97.08%. Conclusions: Therefore, CBIR potentially has a high impact in medical literature search and retrieval.

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The CLEF 2005 Cross–Language Image Retrieval Track

Accessing Multilingual Information Repositories - Lecture Notes in Computer Science ◽

10.1007/11878773_60 ◽

2006 ◽

pp. 535-557 ◽

Cited By ~ 64

Author(s):

Paul Clough ◽

Henning Müller ◽

Thomas Deselaers ◽

Michael Grubinger ◽

Thomas M. Lehmann ◽

...

Keyword(s):

Image Retrieval ◽

Cross Language

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