Skip to main content

Semi-automatic Image Annotation

  • Conference paper
Computer Analysis of Images and Patterns (CAIP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8048))

Included in the following conference series:

Abstract

High quality ground truth data is essential for the development of image recognition systems. General purpose datasets are widely used in research, but they are not suitable as training sets for specialized real-world recognition tasks. The manual annotation of custom ground truth data sets is expensive, but machine learning techniques can be applied to preprocess image data and facilitate annotation. We propose a semi-automatic image annotation process, which clusters images according to similarity in a bag-of-features (BoF) approach. Clusters of images can be efficiently annotated in one go. The system recalculates the clustering continuously, based on partial annotations provided during annotation, by weighting BoF vector elements to increase intra-cluster similarity. Visualization of top-weighted codebook elements allows users to estimate the quality of annotations and of the recalculated clustering.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Moehrmann, J., Heidemann, G.: Efficient development of user-defined image recognition systems. In: Park, J.-I., Kim, J. (eds.) ACCV Workshops 2012, Part I. LNCS, vol. 7728, pp. 242–253. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  2. Moehrmann, J., Heidemann, G.: Efficient Annotation of Image Data Sets for Computer Vision Applications. In: International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications, pp. 2:1–2:6 (2012)

    Google Scholar 

  3. Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: A database and web-based tool for image annotation. International Journal of Computer Vision 77(1), 157–173 (2008)

    Article  Google Scholar 

  4. von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: ACM CHI, pp. 319–326 (2004)

    Google Scholar 

  5. Šimko, J., Bieliková, M.: Personal image tagging: a game-based approach. In: Proceedings of the Intl. Conference on Semantic Systems, pp. 88–93. ACM (2012)

    Google Scholar 

  6. Kumar, N., Kummamuru, K.: Semi-supervised clustering with metric learning using relative comparisons. IEEE Transactions on Knowledge and Data Engineering 20(4), 496–503 (2008)

    Article  Google Scholar 

  7. Cai, H., Yan, F., Mikolajczyk, K.: Learning weights for codebook in image classification and retrieval. In: IEEE CVPR, pp. 2320–2327 (2010)

    Google Scholar 

  8. Xing, E., Ng, A., Jordan, M., Russell, S.: Distance metric learning, with application to clustering with side-information. Advances in Neural Information Processing Systems 15, 505–512 (2002)

    Google Scholar 

  9. Lee, D., Seung, H.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)

    Article  Google Scholar 

  10. Chen, Y., Rege, M., Dong, M., Hua, J.: Non-negative matrix factorization for semi-supervised data clustering. Knowledge and Information Systems 17(3), 355–379 (2008)

    Article  Google Scholar 

  11. Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)

    Google Scholar 

  12. Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)

    Article  Google Scholar 

  13. Manjunath, B.S., Ohm, J.-R., Vasudevan, V.V., Yamada, A.: Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology 11(6), 703–715 (2001)

    Article  Google Scholar 

  14. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)

    Article  Google Scholar 

  15. Jiang, Y.-G., Yang, J., Ngo, C.-W., Hauptmann, A.G.: Representations of keypoint-based semantic concept detection: A comprehensive study. IEEE Transactions on Multimedia 12(1), 42–53 (2010)

    Article  Google Scholar 

  16. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: IEEE CVPR, vol. 2, pp. 2169–2178 (2006)

    Google Scholar 

  17. Fruchterman, T., Reingold, E.: Graph drawing by force-directed placement. Software: Practice and Experience 21(11), 1129–1164 (1991)

    Article  Google Scholar 

  18. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Workshop on Generative-Model Based Vision (2004)

    Google Scholar 

  19. Opelt, A., Pinz, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(3), 416–431 (2006b)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Moehrmann, J., Heidemann, G. (2013). Semi-automatic Image Annotation. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40246-3_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40245-6

  • Online ISBN: 978-3-642-40246-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics