The MedGIFT Group at ImageCLEF 2009

@inproceedings{Zhou2009TheMG,
  title={The MedGIFT Group at ImageCLEF 2009},
  author={Xin Zhou and Ivan Eggel and Henning M{\"u}ller},
  booktitle={CLEF},
  year={2009}
}
This article describes the participation of the MedGIFT research group at the 2008 ImageCLEFmed image retrieval benchmark. We concentrated on the two tasks concerning medical imaging. The visual information analysis is mainly based on the GNU Image Finding Tool (GIFT). Other information such as textual information and aspect ratio were integrated to improve our results. The main techniques are similar to past years, with tuning a few parameters to improve results. For the visual tasks it… 

The MedGIFT Group at ImageCLEF 2008

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    2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
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A classification based auto annotation of tomato images for providing semantic tags is proposed, which takes the advantage of progression in machine vision to address the issue of semantic gap in multimodal retrieval.

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The MedGIFT Group at ImageCLEF 2008

For medical image annotation two approaches were tested: one approach is using GIFT for retrieval and kNN (k-Nearest Neighbors) for classification and the other used the Scale-Invariant Feature Transform (SIFT) with a Support VectorMachine (SVM) classifier.

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This paper describes the medical image retrieval and medical image annotation tasks of ImageCLEF 2007. Separate sections describe each of the two tasks, with the participation and an evaluation of

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A mixed strategy to combine the two origins of the MeSH terms should be planned for the next ImageCLEF, while better performances should be obtained in the future by tuning the system with the existing benchmark.