Single-image background removal with entropy filtering

Chang Chieh Cheng*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Background removal is often used for segmentation of the main subject from a photograph. This paper proposes a new method of background removal for a single image. The proposed method uses Shannon entropy to quantify the texture complexity of background and foreground areas. A normalized entropy filter is applied to compute the entropy of each pixel. The pixels can be classified effectively if the entropy distributions of the background and foreground can be distinguished. To optimize performance, the proposed method constructs an image pyramid such that most background pixels can be labeled in a low-resolution image; thus, the computational cost of entropy calculation can be reduced in the image with the original resolution. Connected component labeling is also adopted for denoising to retain the main subject area completely.

Original languageEnglish
Title of host publicationVISAPP
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz, Kadi Bouatouch
PublisherSciTePress
Pages431-438
Number of pages8
ISBN (Electronic)9789897584886
StatePublished - 2021
Event16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021 - Virtual, Online
Duration: 8 Feb 202110 Feb 2021

Publication series

NameVISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Volume4

Conference

Conference16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021
CityVirtual, Online
Period8/02/2110/02/21

Keywords

  • Background Removal
  • Entropy
  • Segmentation
  • Texture Analysis

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