Scaling-invariant boundary image matching using time-series matching techniques

Moon, Yang-Sae; Kim, Bum Soo; Kim, Min Soo; Whang, Kyu-Young

doi:10.1016/j.datak.2010.07.001

Cited by 21 publications

(15 citation statements)

References 37 publications

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“…Recently, owing to advances in IT technology and an increase in spatio-temporal data, there have been many research efforts on time-series matching to exploit large time-series databases [1]- [3], [6], [11]. In addition, there have been several recent attempts to apply these time-series matching techniques to practical applications such as high dimensional indexing [12], image matching [7], [11], and biological sequence matching [8].…”

Section: Introductionmentioning

confidence: 99%

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Partial denoising boundary image matching using time-series matching techniques

Kim

Gil

Choi

et al. 2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

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Removing noise, called denoising, is an essential factor for achieving the intuitive and accurate results in boundary image matching. This paper deals with a partial denoising problem that tries to allow a limited amount of noise embedded in boundary images. To solve this problem, we first define partial denoising time-series that can be generated from an original image time-series by removing a variety of partial noises. We then propose an efficient mechanism that quickly obtains those partial denoising time-series in the time-series domain rather than the image domain. Next, we present the partial denoising distance, which is the minimum distance from a query timeseries to all possible partial denoising time-series generated from a data time-series. We then use this partial denoising distance as a similarity measure in boundary image matching. Using the partial denoising distance, however, incurs a severe computational overhead since there are a large number of partial denoising time-series to be considered. To solve this problem, we derive a tight lower bound for the partial denoising distance and formally prove its correctness. We also propose partial denoising boundary image matching exploiting the partial denoising distance in boundary image matching. Through extensive experiments, we finally show that our lower bound-based approach improves search performance by up to an order of magnitude in partial denoising-based boundary image matching.

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Section: Introductionmentioning

confidence: 99%

“…In addition, there have been several recent attempts to apply these time-series matching techniques to practical applications such as high dimensional indexing [12], image matching [7], [11], and biological sequence matching [8]. Among these applications, we focus on boundary image matching for a large image database.…”

Section: Introductionmentioning

confidence: 99%

Partial denoising boundary image matching using time-series matching techniques

Kim

Gil

Choi

et al. 2015

2015 International Conference on Big Data and Smart Computing (BIGCOMP)

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“…The second data set is a mixed database consisting of 10,000 images, and we use it for the k-NN search experiment. More precisely, it is composed of 1,000 airplane images, 3,000 baseball images, 1,200 flower images, 3,000 bird images, 800 motorcycle images, and 1,000 guitar images, which are collected from the Web [6]. In k-NN search, the parameter p of WSLPM is not used, and we thus do not specify its value in the experimental result.…”

Section: Experimental Evaluation Of Space Filling Curvesmentioning

confidence: 99%

“…Converting images to time-series facilitates easier distance computation and use of indexes, and this makes it feasible to perform the image matching even on a large image database [6]. We can use a space filling curve (SFC in short) to convert an image of size n × n to a time-series of length n 2 , and we define this time-series as a histogram sequence.…”

Section: Introductionmentioning

confidence: 99%

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

Lee

Kim

Choi

et al. 2013

IEICE Trans. Inf. & Syst.

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SUMMARYHistogram sequences represent high-dimensional timeseries converted from images by space filling curves (SFCs). To overcome the high-dimensionality nature of histogram sequences (e.g., 10 6 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.

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