ImageCLEF

1. Front Matter

   Pages I-XXVIII

   PDF

2. Introduction

   1. Front Matter

      Pages 1-2

      PDF

   2. Seven Years of Image Retrieval Evaluation

      • Paul Clough, Henning Müller, Mark Sanderson

      Pages 3-18

   3. Data Sets Created in ImageCLEF

      • Michael Grubinger, Stefanie Nowak, Paul Clough

      Pages 19-43

   4. Creating Realistic Topics for Image Retrieval Evaluation

      • Henning Müller

      Pages 45-61

   5. Relevance Judgments for Image Retrieval Evaluation

      • Jayashree Kalpathy–Cramer, Steven Bedrick, William Hersh

      Pages 63-80

   6. Performance Measures Used in Image Information Retrieval

      • Mark Sanderson

      Pages 81-94

   7. Fusion Techniques for Combining Textual and Visual Information Retrieval

      • Adrien Depeursinge, Henning Müller

      Pages 95-114

3. Track Reports

   1. Front Matter

      Pages 115-116

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   2. Interactive Image Retrieval

      • Jussi Karlgren, Julio Gonzalo

      Pages 117-139

   3. Photographic Image Retrieval

      • Monica Lestari Paramita, Michael Grubinger

      Pages 141-162

   4. The Wikipedia Image Retrieval Task

      • Theodora Tsikrika, Jana Kludas

      Pages 163-183

   5. The Robot Vision Task

      • Andrzej Pronobis, Barbara Caputo

      Pages 185-198

   6. Object and Concept Recognition for Image Retrieval

      • Stefanie Nowak, Allan Hanbury, Thomas Deselaers

      Pages 199-219

   7. The Medical Image Classification Task

      • Tatiana Tommasi, Thomas Deselaers

      Pages 221-238

   8. The Medical Image Retrieval Task

      • Henning Müller, Jayashree Kalpathy–Cramer

      Pages 239-257

4. Participant reports

   1. Front Matter

      Pages 259-260

      PDF

   2. Expansion and Re–ranking Approaches for Multimodal Image Retrieval using Text–based Methods

      • Adil Alpkocak, Deniz Kilinc, Tolga Berber

      Pages 261-275

   3. Revisiting Sub–topic Retrieval in the ImageCLEF 2009 Photo Retrieval Task

      • Teerapong Leelanupab, Guido Zuccon, Joemon M. Jose

      Pages 277-294

   4. Knowledge Integration using Textual Information for Improving ImageCLEF Collections

      • Manuel Carlos Díaz–Galiano, Miguel Ángel García–Cumbreras, María Teresa Martín–Valdivia, Arturo Montejo-Ráez

      Pages 295-313

The pervasive creation and consumption of content, especially visual content, is ingrained into our modern world. We’re constantly consuming visual media content, in printed form and in digital form, in work and in leisure pursuits. Like our cave– man forefathers, we use pictures to record things which are of importance to us as memory cues for the future, but nowadays we also use pictures and images to document processes; we use them in engineering, in art, in science, in medicine, in entertainment and we also use images in advertising. Moreover, when images are in digital format, either scanned from an analogue format or more often than not born digital, we can use the power of our computing and networking to exploit images to great effect. Most of the technical problems associated with creating, compressing, storing, transmitting, rendering and protecting image data are already solved. We use - cepted standards and have tremendous infrastructure and the only outstanding ch- lenges, apart from managing the scale issues associated with growth, are to do with locating images. That involves analysing them to determine their content, clas- fying them into related groupings, and searching for images. To overcome these challenges we currently rely on image metadata, the description of the images, - ther captured automatically at creation time or manually added afterwards.

• Annotation
• Image Retrieval
• Medical Image Processing
• Multimedia Retrieval
• Performance Evaluation
• Robot Vision
• Text Retrieval
• classification
• information retrieval
• media retrieval
• performance

• HES-SO Business Information Systems, Sierre, Switzerland

  Henning Müller

• Dept. Information Studies, University of Sheffield, Sheffield, United Kingdom

  Paul Clough

• , Computer Vision Lab/ETF-C 113.2, ETH Zürich, Zürich, Switzerland

  Thomas Deselaers

• Idiap Research Institute, Martigny, Switzerland

  Barbara Caputo

Policies and ethics