Similarity-Based Subsequence Search in Image Sequence Databases


The KIPS Transactions:PartD, Vol. 10, No. 3, pp. 501-512, Jun. 2003
10.3745/KIPSTD.2003.10.3.501,   PDF Download:

Abstract

This paper proposes an indexing technique for fast retrieval of similar image subsequences using the multi-dimensional time warping distance. The time warping distance is a more suitable similarity measure than Lp distance in many applications where sequences may be of different lengths and/or different sampling rates. Our indexing scheme employs a disk-based suffix tree as an index structure and uses a lower-bound distance function to filter out dissimilar subsequences without false dismissals. It applies the normalization for an easier control of relative weighting of feature dimensions and the discretization to compress the index tree. Experiments on medical and synthetic image sequences verify that the proposed method significantly outperforms the naive method and scales well in a large volume of image sequence databases.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
I. B. Kim and S. H. Park, "Similarity-Based Subsequence Search in Image Sequence Databases," The KIPS Transactions:PartD, vol. 10, no. 3, pp. 501-512, 2003. DOI: 10.3745/KIPSTD.2003.10.3.501.

[ACM Style]
In Bum Kim and Sang Hyun Park. 2003. Similarity-Based Subsequence Search in Image Sequence Databases. The KIPS Transactions:PartD, 10, 3, (2003), 501-512. DOI: 10.3745/KIPSTD.2003.10.3.501.