Evaluation of Space Filling Curves for Lower-Dimensional Transformation of Image Histogram Sequences

• LEE Jeonggon

  Department of Computer Science, Kangwon National University

• KIM Bum-Soo

  Department of Computer Science, Kangwon National University

• CHOI Mi-Jung

  Department of Computer Science, Kangwon National University

• MOON Yang-Sae

  Department of Computer Science, Kangwon National University

抄録

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.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E96.D (10), 2277-2281, 2013

  一般社団法人 電子情報通信学会

キーワード

• data mining
• time-series data
• space filling curve
• lower-dimensional transformation

詳細情報

• CRID: 1390001204378237568
• NII論文ID: 130004519211
• DOI: 10.1587/transinf.e96.d.2277
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E96.D/10/E96.D_2277/_pdf
• 本文言語コード: en
• データソース種別:
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  • Crossref
  • CiNii Articles
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