A Scale-Reductive Pooling with Majority-Take-All for Salient Object Detection

Chin Han Shen, Yang Jie Chen, Hsu Feng Hsiao

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

Abstract

With the rapid development of hardware and related technologies, salient object detection based on deep learning methods has become one of the popular research topics in computer vision applications. For the detection focused on the integrity of salient objects, edge accuracy of objects is one of the important indicators in the evaluation of visual saliency detection. However, in deep learning-based methods, complex networks and large amounts of data are usually required to achieve good boundary accuracy. To solve this issue, a scale-reductive pooling approach with clustering-based majority-take-all strategy is proposed in this paper. According to the experimental results, we show that the prediction results are improved with reasonable quantity of superpixels.

Original languageEnglish
Title of host publicationIEEE International Symposium on Circuits and Systems, ISCAS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3309-3313
Number of pages5
ISBN (Electronic)9781665484855
DOIs
StatePublished - 2022
Event2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 - Austin, United States
Duration: 27 May 20221 Jun 2022

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2022-May
ISSN (Print)0271-4310

Conference

Conference2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
Country/TerritoryUnited States
CityAustin
Period27/05/221/06/22

Keywords

  • deep learning
  • salient object detection
  • superpixel pooling

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