Easing Erroneous Translations in Cross-Language Image Retrieval Using Word Associations

2006 ◽  
pp. 582-591 ◽  
Author(s):  
Masashi Inoue
Keyword(s):  
Image Retrieval ◽  
Word Associations ◽  
Cross Language

scholarly journals Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Xian-Hua Han ◽  
Yen-Wei Chen
Keyword(s):  
Image Retrieval ◽  
Imaging Modality ◽  
Kernel Functions ◽  
Visual Features ◽  
Color Intensity ◽  
Global Features ◽  
Retrieval Task ◽  
Block Based ◽  
Cross Language ◽  
Textual Feature

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.

Content-based Image Retrieval for Scientific Literature Access

2009 ◽  
Vol 48 (04) ◽  
pp. 371-380 ◽  
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.

scholarly journals The CLEF 2005 Cross–Language Image Retrieval Track

2006 ◽  
pp. 535-557 ◽  
Author(s):  
Paul Clough ◽  
Henning Müller ◽  
Thomas Deselaers ◽  
Michael Grubinger ◽  
Thomas M. Lehmann ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Cross Language

User Behaviour and Lexical Ambiguity in Cross-Language Image Retrieval

2010 ◽  
pp. 29-36 ◽  
Author(s):  
Borja Navarro-Colorado ◽  
Marcel Puchol-Blasco ◽  
Rafael M. Terol ◽  
Sonia Vázquez ◽  
Elena Lloret
Keyword(s):  
Image Retrieval ◽  
Lexical Ambiguity ◽  
User Behaviour ◽  
Cross Language
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