Addressing the ImageClef 2009 Challenge using a patch-based visual words

Uri Avni; J. Goldberger; Hayit Greenspan

Addressing the ImageClef 2009 Challenge using a patch-based visual words

Uri Avni
, J. Goldberger
, Hayit Greenspan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper describes our participation at the ImageClef 2009 medical annotation task. In this task we have used the bag-of-words approach for image representation. We submitted one run, using support-vector-machines trained on the visual word histograms in multiple scales. In this task our result ranked first, with error score of 852.8.

Original language	American English
Title of host publication	The Cross Language Evaluation Forum Workshop (CLEF)
State	Published - 2009

Bibliographical note

Place of conference:Italy

Cite this

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title = "Addressing the ImageClef 2009 Challenge using a patch-based visual words",

abstract = "This paper describes our participation at the ImageClef 2009 medical annotation task. In this task we have used the bag-of-words approach for image representation. We submitted one run, using support-vector-machines trained on the visual word histograms in multiple scales. In this task our result ranked first, with error score of 852.8.",

author = "Uri Avni and J. Goldberger and Hayit Greenspan",

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AU - Avni, Uri

AU - Goldberger, J.

AU - Greenspan, Hayit

N1 - Place of conference:Italy

PY - 2009

Y1 - 2009

N2 - This paper describes our participation at the ImageClef 2009 medical annotation task. In this task we have used the bag-of-words approach for image representation. We submitted one run, using support-vector-machines trained on the visual word histograms in multiple scales. In this task our result ranked first, with error score of 852.8.

AB - This paper describes our participation at the ImageClef 2009 medical annotation task. In this task we have used the bag-of-words approach for image representation. We submitted one run, using support-vector-machines trained on the visual word histograms in multiple scales. In this task our result ranked first, with error score of 852.8.

UR - https://scholar.google.co.il/scholar?q=Addressing+the+ImageClef+2009+Challenge+using+a+patch-based+visual+words&btnG=&hl=en&as_sdt=0%2C5

M3 - Conference contribution

BT - The Cross Language Evaluation Forum Workshop (CLEF)

ER -

Addressing the ImageClef 2009 Challenge using a patch-based visual words

Abstract

Bibliographical note

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