Meiram Murzabulatov | Searcharxiv

arXiv2024

Testing Connectedness of Images

We investigate algorithms for testing whether an image is connected. Given a proximity parameter $ε\in(0,1)$ and query access to a black-and-white image represented by an $n\times n$ matrix of Boolean pixel values, a (1-sided error) connectedness tester accepts if the image is connected and rejects with probability at least 2/3 if the image is $ε$-far from connected. We show that connectedness can be tested nonadaptively with $O(\frac 1{ε^2})$ queries and adaptively with $O(\frac{1}{ε^{3/2}} \sqrt{\log\frac{1}ε})$ queries. The best connectedness tester to date, by Berman, Raskhodnikova, and Yaroslavtsev (STOC 2014) had query complexity $O(\frac 1{ε^2}\log \frac 1ε)$ and was adaptive. We also prove that every nonadaptive, 1-sided error tester for connectedness must make $Ω(\frac 1ε\log \frac 1ε)$ queries.

cs.DS↗

arXiv2016

Testing Unateness of Real-Valued Functions

We give a unateness tester for functions of the form $f:[n]^d\rightarrow R$, where $n,d\in \mathbb{N}$ and $R\subseteq \mathbb{R}$ with query complexity $O(\frac{d\log (\max(d,n))}ε)$. Previously known unateness testers work only for Boolean functions over the domain $\{0,1\}^d$. We show that every unateness tester for real-valued functions over hypergrid has query complexity $Ω(\min\{d, |R|^2\})$. Consequently, our tester is nearly optimal for real-valued functions over $\{0,1\}^d$. We also prove that every nonadaptive, 1-sided error unateness tester for Boolean functions needs $Ω(\sqrt{d}/ε)$ queries. Previously, no lower bounds for testing unateness were known.

cs.DS↗

arXiv2016

Tolerant Testers of Image Properties

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 $1/ε$ 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.)

cs.DS↗