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Robust Feature Bundling

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7674)

Abstract

In this work we present a feature bundling technique that aggregates individual local features with features from their spatial neighborhood into bundles. The resulting bundles carry more information of the underlying image content than single visual words. As in practice an exact search for such bundles is infeasible, we employ a robust approximate similarity search with min-hashing in order to retrieve images containing similar bundles.

We demonstrate the benefits of these bundles for small object retrieval, i.e. logo recognition, and generic image retrieval. Multiple bundling strategies are explored and thoroughly evaluated on three different datasets.

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  • DOI: 10.1007/978-3-642-34778-8_5
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References

  1. Chum, O., Matas, J.: Large-scale discovery of spatially related images. PAMI, 371–377 (2010)

    Google Scholar 

  2. Chum, O., Perdoch, M., Matas, J.: Geometric min-Hashing: Finding a (thick) needle in a haystack. In: Proc. CVPR (2009)

    Google Scholar 

  3. Chum, O., Philbin, J., Isard, M.: Scalable near identical image and shot detection. In: Proc. CIVR (2007)

    Google Scholar 

  4. Chum, O., Philbin, J., Zisserman, A.: Near duplicate image detection: min-hash and tf-idf weighting. In: Proc. BMVC (2008)

    Google Scholar 

  5. Jégou, H., Douze, M., Schmid, C.: Improving Bag-of-Features for Large Scale Image Search. IJCV 87(3), 316–336 (2009)

    CrossRef  Google Scholar 

  6. Lee, D.C., Ke, Q., Isard, M.: Partition Min-Hash for Partial Duplicate Image Discovery. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 648–662. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  7. Muja, M., Lowe, D.: Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. In: Proc. VISAPP (2009)

    Google Scholar 

  8. Nistér, D., Stewénius, H.: Scalable Recognition with a Vocabulary Tree. In: Proc. CVPR (2006)

    Google Scholar 

  9. Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Proc. CVPR (2007)

    Google Scholar 

  10. Romberg, S., Garcia Pueyo, L., Lienhart, R., van Zwol, R.: Scalable Logo Recognition in Real-World Images. In: Proc. ICMR (2011)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Romberg, S., August, M., Ries, C.X., Lienhart, R. (2012). Robust Feature Bundling. In: , et al. Advances in Multimedia Information Processing – PCM 2012. PCM 2012. Lecture Notes in Computer Science, vol 7674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34778-8_5

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  • DOI: https://doi.org/10.1007/978-3-642-34778-8_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34777-1

  • Online ISBN: 978-3-642-34778-8

  • eBook Packages: Computer ScienceComputer Science (R0)