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Spatial Locality Preservation Metric for Constructing Histogram Sequences  

Lee, Jeonggon (Department of Computer Science, Kangwon National University)
Kim, Bum-Soo (Department of Computer Science, Kangwon National University)
Moon, Yang-Sae (Department of Computer Science, Kangwon National University)
Choi, Mi-Jung (Department of Computer Science, Kangwon National University)
Publication Information
Journal of Information Technology and Architecture / v.10, no.1, 2013 , pp. 79-91 More about this Journal
Abstract
This paper proposes a systematic methodology that could be used to decide which one shows the best performance among space filling curves (SFCs) in applying lower-dimensional transformations to histogram sequences. A histogram sequence represents a time-series converted from an image by the given SFC. Due to the high-dimensionality nature, histogram sequences are very difficult to be stored and searched in their original form. To solve this problem, we generally use lower-dimensional transformations, which produce lower bounds among high dimensional sequences, but the tightness of those lower-bounds is highly affected by the types of SFC. In this paper, we attack a challenging problem of evaluating which SFC shows the better performance when we apply the lower-dimensional transformation to histogram sequences. For this, we first present a concept of spatial locality, which comes from an intuition of "if the entries are adjacent in a histogram sequence, their corresponding cells should also be adjacent in its original image." We also propose spatial locality preservation metric (slpm in short) that quantitatively evaluates spatial locality and present its formal computation method. We then evaluate five SFCs from the perspective of slpm and verify that this evaluation result concurs with the performance evaluation of lower-dimensional transformations in real image matching. Finally, we perform k-NN (k-nearest neighbors) search based on lower-dimensional transformations and validate accuracy of the proposed slpm by providing that the Hilbert-order with the highest slpm also shows the best performance in k-NN search.
Keywords
Data Mining; Time-Series Data; Space Filling Curve; Lower-Dimensional Transformation;
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Times Cited By KSCI : 2  (Citation Analysis)
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