Advertisement

Accurate Image Retrieval Using Content Dissimilarity: Performance Enhancement by Indexing

  • Sonwane Priyanka
  • S. G. Shikalpure
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 250)

Abstract

This paper introduces the content dissimilarity measure, which significantly improves the accuracy of bag-of-features based image search. Our measure takes into account the local distribution of the vectors and iteratively estimates distance update terms in the spirit of Sinkhorn’s scaling algorithm, thereby modifying the neighbourhood structure. Experimental results show that our approach gives significantly better results than a standard distance method. This paper also evaluates the impact of a large number of parameters, including the number of descriptors, the clustering method, the visual vocabulary size, and the distance measure. In particular, using a large number of descriptors is interesting only when using our dissimilarity measure. We have also evaluated two novel variants: multiple assignment and rank aggregation. They are shown to further improve accuracy at the cost of higher memory usage and lower efficiency. We also combine a indexing technique for achieving efficient and effective retrieval performance.

Keywords

Image retrieval Distance regularization Clustering Indexing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lowe, D.: Distinctive Image Features from Scale Invariant Keypoints. Int’l J. Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Mikolajczyk, K., Schmid, C.: Scale and Affine Invariant Interests. Int’l J. Computer Vision 60(1), 63–86 (2004)CrossRefGoogle Scholar
  3. 3.
    Nistér, D., Stewénius, H.: Scalable Recognition with a Vocabulary Tree. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2161–2168 (2006)Google Scholar
  4. 4.
    Sivic, J., Zisserman, A.: Video Google: A Text Retrieval Approach to Object Matching in Videos. In: Proc. IEEE Int’l Conf. Computer Vision, pp. 1470–1477 (2003)Google Scholar
  5. 5.
    Chopra, S., Hadsell, R., LeCun, Y.: Learning a Similarity Metric Discriminatively. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 539–546 (2005)Google Scholar
  6. 6.
    Stewénius, H., Nistér, D.: Object Recognition Benchmark (2008), http://vis.uky.edu/%7Estewe/ukbench/
  7. 7.
    Fagin, R., Kumar, R., Sivakumar, D.: Efficient Similarity Search and Classification via Rank Aggregation. In: Proc. ACM SIGMOD, pp. 301–312 (2003)Google Scholar
  8. 8.
    Kemper, A.: Algorithms and Datastructures for Database Systems. In: SS 2003 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sonwane Priyanka
    • 1
  • S. G. Shikalpure
    • 2
  1. 1.Faculty of Information TechnologyGovernment polytechnicPuneIndia
  2. 2.Faculty of computer EngineeringGovernment Engineering CollegeAurangabadIndia

Personalised recommendations