An efficiently computable metric for comparing polygonal shapes

Arkin, Esther M.; Chew, L. Paul; Huttenlocher, Daniel P.; Kedem, Klara; Mitchell, Joseph S. B.

doi:10.1109/34.75509

Cited by 535 publications

(289 citation statements)

References 9 publications

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“…To better re¯ect any deformation in the shape of the objects a second criterion is invoked. The boundary function (BF) metric is based on the turning angle function of the objects [35,38]. The rationale was that the CE criterion did not suf®ciently re¯ect the deformation of the objects.…”

Section: Quantitative Resultsmentioning

confidence: 99%

Image segmentation by relaxation using constraint satisfaction neural network

Kurugöllü

Sankur

Harmanci

2002

Image and Vision Computing

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Section: Quantitative Resultsmentioning

confidence: 99%

Image segmentation by relaxation using constraint satisfaction neural network

Kurugöllü

Sankur

Harmanci

2002

Image and Vision Computing

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“…The requirement for anonymization is not just similarity regarding shape, but also spatial and temporal closeness. Some typical distances for trajectories include the Euclidean distance, the Hausdorff distance [40], the Fréchet distance [4], the turning point distance [5], and distances based on time series [29] -e.g., dynamic time warping (DTW), short time series (STS)-and on edit distance [9] -e.g, edit distance with real penalty (ERP), longest common sub-sequence (LCSS), and the edit distance on real sequences (EDR) discussed next.…”

Section: Trajectory Similarity Measuresmentioning

confidence: 99%

Microaggregation- and permutation-based anonymization of movement data

Domingo-Ferrer

Trujillo-Rasúa

2012

Information Sciences

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Movement data, that is, trajectories of mobile objects, are automatically collected in huge quantities by technologies such as GPS, GSM or RFID, among others. Publishing and exploiting such data is essential to improve transportation, to understand the dynamics of the economy in a region, etc. However, there are obvious threats to the privacy of individuals if their trajectories are published in a way which allows re-identification of the individual behind a trajectory. We contribute to the literature on privacy-preserving publication of trajectories by presenting a distance measure for trajectories which naturally considers both spatial and temporal aspects of trajectories, is computable in polynomial time, and can cluster trajectories not defined over the same time span. Our distance measure can be naturally instantiated using other existing similarity measures for trajectories that are appropriate for anonymization purposes. Then, we propose two heuristics for trajectory anonymization which yield anonymized trajectories formed by fully accurate true original locations. The first heuristic is based on trajectory microaggregation using the above distance and on location permutation; it effectively achieves trajectory k-anonymity. The second heuristic is based only on location permutation; it gives up trajectory k-anonymity and aims at location k-diversity. The strong point of the second heuristic is that it takes into account reachability constraints when computing anonymized trajectories. Experimental results on a synthetic data set and a real-life data set are presented; for similar privacy protection levels and most reasonable parameter choices, our two methods offer better utility than comparable previous proposals in the literature.

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“…Arkin et al proposed a method based on L 2 distance [1] as a metric for comparing polygonal shapes. Later, Latecki et al [9] defined shape similarity measure related to the one of Arkin et al…”

Section: Similarity Of Polygonal Shapesmentioning

confidence: 99%

“…On the other hand, CM ζ can be also determined in linear time based on the notion of critical events presented in [1].…”

Section: Proposed Algorithmmentioning

confidence: 99%