Locating salient group-structured image features via adaptive compressive sensing

Xingguo Li, Jarvis Haupt

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

7 Scopus citations

Abstract

In this paper we consider the task of locating salient group-structured features in potentially high-dimensional images; the salient feature detection here is modeled as a Robust Principal Component Analysis problem, in which the aim is to locate groups of outlier columns embedded in an otherwise low rank matrix. We adapt an adaptive compressive sensing method from our own previous work (which examined the task of identifying arbitrary sets of outlier columns in large matrices) to settings where the outlier columns occur in groups, and establish theoretical results certifying that accurate group-structured inference is achievable using very few linear measurements of the image, subject to some (arguably) minor structural assumptions on the image itself. We also demonstrate, through extensive numerical simulations, our proposed algorithm in a salient object detection task, and show that it simultaneously achieves low sample and computational complexity, while exhibiting performance comparable to state-of-the-art methods that acquire and process the entire image.

Original languageEnglish (US)
Title of host publication2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages393-397
Number of pages5
ISBN (Electronic)9781479975914
DOIs
StatePublished - Feb 23 2016
EventIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 - Orlando, United States
Duration: Dec 13 2015Dec 16 2015

Publication series

Name2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

Other

OtherIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
CountryUnited States
CityOrlando
Period12/13/1512/16/15

Fingerprint Dive into the research topics of 'Locating salient group-structured image features via adaptive compressive sensing'. Together they form a unique fingerprint.

Cite this