Scalable Indexing for Perceptual Data

  • Arun Qamra
  • Edward Y. Chang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4577)


In recent years, multimedia objects such as images, video, and audio are becoming increasingly widespread. Many applications require content-based retrieval to be performed, and measurement of distance is a key component in such scenarios. The nature of multimedia requires perceptual similarity to be captured when computing distance between objects. Measures such as the Euclidean distance, which utilize all attributes of a pair of objects, do not perform very well. Instead, distance measures that use partial matches between objects have been found to perform significantly better. This is because, two multimedia objects can be considered perceptually similar when some respects closely match, even when they are very different in other respects. Existing distance measures that capture partial similarity have limitations, such as their non-metric nature, which makes scalable indexing challenging. In this paper, we propose the Partial Match Function, a distance measure that performs well for perceptual data, and allows efficient indexing.


Distance Measure Triangle Inequality Partial Match Perceptual Similarity Candidate Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Qamra, A., Meng, Y., Chang, E.Y.: Enhanced perceptual distance functions and indexing for image replica recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 379–391 (2005)CrossRefGoogle Scholar
  2. Tung, A.K.H., Zhang, R., Koudas, N., Ooi, B.C.: Similarity searching: a matching based approach. In: VLDB (2006)Google Scholar
  3. Goldstone, R.L.: Similarity, interactive activation, and mapping. Journal of Experimental Psychology: Learning, Memory, and Cognition 20, 3–28 (1994)CrossRefGoogle Scholar
  4. Goh, K., Li, B., Chang, E.Y.: Dyndex: a dynamic and non-metric space indexer. In: ACM International Conference on Multimedia (2002)Google Scholar
  5. Weber, R., Schek, H.J., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: VLDB (1998)Google Scholar
  6. Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional spaces. In: ICDT (2001)Google Scholar
  7. Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is nearest neighbors meaningful? In: ICDT (1999)Google Scholar
  8. Hinneburg, A., Aggarwal, C.C., Keim, D.A.: What is the nearest neighbor in high dimensional spaces? In: VLDB (2000)Google Scholar
  9. Li, B., Chang, E., Wu, Y.: Discovery of a perceptual distance function for measuring image similarity. ACM Multimedia Journal Special Issue on Content-Based Image Retrieval 8(6), 512–522 (2003)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Arun Qamra
    • 1
  • Edward Y. Chang
    • 2
  1. 1.Dept of Computer Science, University of California Santa Barbara 
  2. 2.Google Research 

Personalised recommendations