Evaluation of Space Filling Curves for Lower-Dimensional Transformation of Image Histogram Sequences
Summary :
Histogram sequences represent high-dimensional time-series converted from images by space filling curves (SFCs). To overcome the high-dimensionality nature of histogram sequences (e.g., 106 dimensions for a 1024×1024 image), we often use lower-dimensional transformations, but the tightness of their lower-bounds is highly affected by the types of SFCs. 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 and propose spatial locality preservation metric (SLPM in short). We then evaluate five well-known SFCs from the perspective of SLPM and verify that the evaluation result concurs with the actual transformation performance. Finally, we empirically validate the accuracy of SLPM by providing that the Hilbert-order with the highest SLPM also shows the best performance in k-NN (k-nearest neighbors) search.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.10 pp.2277-2281
- Publication Date
- 2013/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.2277
- Type of Manuscript
- LETTER
- Category
- Data Engineering, Web Information Systems
Authors
Jeonggon LEE
Kangwon National University
Bum-Soo KIM
Kangwon National University
Mi-Jung CHOI
Kangwon National University
Yang-Sae MOON
Kangwon National University
Keyword
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Jeonggon LEE, Bum-Soo KIM, Mi-Jung CHOI, Yang-Sae MOON, "Evaluation of Space Filling Curves for Lower-Dimensional Transformation of Image Histogram Sequences" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 10, pp. 2277-2281, October 2013, doi: 10.1587/transinf.E96.D.2277.
Abstract: Histogram sequences represent high-dimensional time-series converted from images by space filling curves (SFCs). To overcome the high-dimensionality nature of histogram sequences (e.g., 106 dimensions for a 1024×1024 image), we often use lower-dimensional transformations, but the tightness of their lower-bounds is highly affected by the types of SFCs. 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 and propose spatial locality preservation metric (SLPM in short). We then evaluate five well-known SFCs from the perspective of SLPM and verify that the evaluation result concurs with the actual transformation performance. Finally, we empirically validate the accuracy of SLPM by providing that the Hilbert-order with the highest SLPM also shows the best performance in k-NN (k-nearest neighbors) search.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.2277/_p
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@ARTICLE{e96-d_10_2277,
author={Jeonggon LEE, Bum-Soo KIM, Mi-Jung CHOI, Yang-Sae MOON, },
journal={IEICE TRANSACTIONS on Information},
title={Evaluation of Space Filling Curves for Lower-Dimensional Transformation of Image Histogram Sequences},
year={2013},
volume={E96-D},
number={10},
pages={2277-2281},
abstract={Histogram sequences represent high-dimensional time-series converted from images by space filling curves (SFCs). To overcome the high-dimensionality nature of histogram sequences (e.g., 106 dimensions for a 1024×1024 image), we often use lower-dimensional transformations, but the tightness of their lower-bounds is highly affected by the types of SFCs. 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 and propose spatial locality preservation metric (SLPM in short). We then evaluate five well-known SFCs from the perspective of SLPM and verify that the evaluation result concurs with the actual transformation performance. Finally, we empirically validate the accuracy of SLPM by providing that the Hilbert-order with the highest SLPM also shows the best performance in k-NN (k-nearest neighbors) search.},
keywords={},
doi={10.1587/transinf.E96.D.2277},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Evaluation of Space Filling Curves for Lower-Dimensional Transformation of Image Histogram Sequences
T2 - IEICE TRANSACTIONS on Information
SP - 2277
EP - 2281
AU - Jeonggon LEE
AU - Bum-Soo KIM
AU - Mi-Jung CHOI
AU - Yang-Sae MOON
PY - 2013
DO - 10.1587/transinf.E96.D.2277
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2013
AB - Histogram sequences represent high-dimensional time-series converted from images by space filling curves (SFCs). To overcome the high-dimensionality nature of histogram sequences (e.g., 106 dimensions for a 1024×1024 image), we often use lower-dimensional transformations, but the tightness of their lower-bounds is highly affected by the types of SFCs. 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 and propose spatial locality preservation metric (SLPM in short). We then evaluate five well-known SFCs from the perspective of SLPM and verify that the evaluation result concurs with the actual transformation performance. Finally, we empirically validate the accuracy of SLPM by providing that the Hilbert-order with the highest SLPM also shows the best performance in k-NN (k-nearest neighbors) search.
ER -
English
