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Reliability and effectiveness of clickthrough data for automatic image annotation

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Abstract

Automatic image annotation using supervised learning is performed by concept classifiers trained on labelled example images. This work proposes the use of clickthrough data collected from search logs as a source for the automatic generation of concept training data, thus avoiding the expensive manual annotation effort. We investigate and evaluate this approach using a collection of 97,628 photographic images. The results indicate that the contribution of search log based training data is positive despite their inherent noise; in particular, the combination of manual and automatically generated training data outperforms the use of manual data alone. It is therefore possible to use clickthrough data to perform large-scale image annotation with little manual annotation effort or, depending on performance, using only the automatically generated training data. An extensive presentation of the experimental results and the accompanying data can be accessed at http://olympus.ee.auth.gr/~diou/civr2009/.

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Notes

  1. http://www-nlpir.nist.gov/projects/trecvid/

  2. http://images.google.com/imagelabeler/

  3. http://www.youtube.com/

  4. http://www.flickr.com/

  5. http://www.pascal-network.org/challenges/VOC/

  6. http://dbappl.cs.utwente.nl/pftijah/

  7. http://www.belga.be/

  8. http://vitalas.ercim.org/

  9. http://www.iptc.org/

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Acknowledgements

The authors are grateful to the Belga press agency for providing the images and search logs used in this work and to Marco Palomino from the University of Sunderland for the extraction of the text features used. This work was supported by the EU-funded VITALAS project (FP6-045389). Christos Diou is supported by the Greek State Scholarships Foundation (http://www.iky.gr).

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Authors and Affiliations

  1. Centrum Wiskunde & Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands

    Theodora Tsikrika & Arjen P. de Vries

  2. Multimedia Understanding Group, Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Christos Diou & Anastasios Delopoulos

  3. Informatics and Telematics Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Christos Diou & Anastasios Delopoulos

  4. Delft University of Technology, Delft, The Netherlands

    Arjen P. de Vries

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  1. Theodora Tsikrika
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  2. Christos Diou
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  4. Anastasios Delopoulos
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Correspondence to Theodora Tsikrika.

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Tsikrika, T., Diou, C., de Vries, A.P. et al. Reliability and effectiveness of clickthrough data for automatic image annotation. Multimed Tools Appl 55, 27–52 (2011). https://doi.org/10.1007/s11042-010-0584-1

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