Two maximum entropy-based algorithms for running quantile estimation in nonstationary data streams

Arandjelović, Ognjen, Pham, Duc-Son and Venkatesh, Svetha 2015, Two maximum entropy-based algorithms for running quantile estimation in nonstationary data streams, IEEE transactions on circuits and systems for video technology, vol. 25, no. 9, pp. 1469-1479, doi: 10.1109/TCSVT.2014.2376137.

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Title Two maximum entropy-based algorithms for running quantile estimation in nonstationary data streams
Author(s) Arandjelović, Ognjen
Pham, Duc-Son
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Journal name IEEE transactions on circuits and systems for video technology
Volume number 25
Issue number 9
Start page 1469
End page 1479
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-09
ISSN 1051-8215
Keyword(s) histogram
Science & Technology
Engineering, Electrical & Electronic
Summary The need to estimate a particular quantile of a distribution is an important problem that frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many semiautomatic surveillance analytics systems that detect abnormalities in close-circuit television footage using statistical models of low-level motion features. In this paper, we specifically address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited. We make the following several major contributions: 1) we highlight the limitations of approaches previously described in the literature that make them unsuitable for nonstationary streams; 2) we describe a novel principle for the utilization of the available storage space; 3) we introduce two novel algorithms that exploit the proposed principle in different ways; and 4) we present a comprehensive evaluation and analysis of the proposed algorithms and the existing methods in the literature on both synthetic data sets and three large real-world streams acquired in the course of operation of an existing commercial surveillance system. Our findings convincingly demonstrate that both of the proposed methods are highly successful and vastly outperform the existing alternatives. We show that the better of the two algorithms (data-aligned histogram) exhibits far superior performance in comparison with the previously described methods, achieving more than 10 times lower estimate errors on real-world data, even when its available working memory is an order of magnitude smaller.
Language eng
DOI 10.1109/TCSVT.2014.2376137
Field of Research 080109 Pattern Recognition and Data Mining
0906 Electrical And Electronic Engineering
0801 Artificial Intelligence And Image Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
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