Tolerant testers of image properties

Piotr Berman, Meiram Murzabulatov, Sofya Raskhodnikova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

We initiate a systematic study of tolerant testers of image properties or, equivalently, algorithms that approximate the distance from a given image to the desired property (that is, the smallest fraction of pixels that need to change in the image to ensure that the image satisfies the desired property). Image processing is a particularly compelling area of applications for sublinear-time algorithms and, specifically, property testing. However, for testing algorithms to reach their full potential in image processing, they have to be tolerant, which allows them to be resilient to noise. Prior to this work, only one tolerant testing algorithm for an image property (image partitioning) has been published. We design efficient approximation algorithms for the following fundamental questions: What fraction of pixels have to be changed in an image so that it becomes a half-plane? a representation of a convex object? a representation of a connected object? More precisely, our algorithms approximate the distance to three basic properties (being a half-plane, convexity, and connectedness) within a small additive error ϵ, after reading a number of pixels polynomial in 1/- and independent of the size of the image. The running time of the testers for half-plane and convexity is also polynomial in 1/ϵ. Tolerant testers for these three properties were not investigated previously. For convexity and connectedness, even the existence of distance approximation algorithms with query complexity independent of the input size is not implied by previous work. (It does not follow from the VC-dimension bounds, since VC dimension of convexity and connectedness, even in two dimensions, depends on the input size. It also does not follow from the existence of non-tolerant testers.) Our algorithms require very simple access to the input: uniform random samples for the half-plane property and convexity, and samples from uniformly random blocks for connectedness. However, the analysis of the algorithms, especially for convexity, requires many geometric and combinatorial insights. For example, in the analysis of the algorithm for convexity, we define a set of reference polygons Pϵ such that (1) every convex image has a nearby polygon in Pϵ and (2) one can use dynamic programming to quickly compute the smallest empirical distance to a polygon in Pϵ. This construction might be of independent interest.

Original languageEnglish (US)
Title of host publication43rd International Colloquium on Automata, Languages, and Programming, ICALP 2016
EditorsYuval Rabani, Ioannis Chatzigiannakis, Davide Sangiorgi, Michael Mitzenmacher
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959770132
DOIs
StatePublished - Aug 1 2016
Event43rd International Colloquium on Automata, Languages, and Programming, ICALP 2016 - Rome, Italy
Duration: Jul 12 2016Jul 15 2016

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume55
ISSN (Print)1868-8969

Other

Other43rd International Colloquium on Automata, Languages, and Programming, ICALP 2016
CountryItaly
CityRome
Period7/12/167/15/16

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All Science Journal Classification (ASJC) codes

  • Software

Cite this

Berman, P., Murzabulatov, M., & Raskhodnikova, S. (2016). Tolerant testers of image properties. In Y. Rabani, I. Chatzigiannakis, D. Sangiorgi, & M. Mitzenmacher (Eds.), 43rd International Colloquium on Automata, Languages, and Programming, ICALP 2016 [90] (Leibniz International Proceedings in Informatics, LIPIcs; Vol. 55). Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.ICALP.2016.90