Salient Object Detection via Fuzzy Theory and Object-Level Enhancement

Yuan Zhou, Ailing Mao, Shuwei Huo, Jianjun Lei, Sun-Yuan Kung

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

This paper proposes a bottom-up saliency detection method via effective integration of regional saliency measure and object-level information using fuzzy theory. First, we generate an initial saliency map by fusing multiple prior maps. Second, to emphasize the object-level concept of saliency, we further generate many object proposals of the input image. A fuzzy set theory is then applied to measure the objectness score of the object proposals and integrate them into an objectness map. Third, an optimization framework is proposed to effectively fuse various prior saliency cues and object-level information to produce a clean and uniform saliency map as well as to maintain the salient object completeness. Experimental studies in several benchmark datasets confirmed the superiority of the proposed method over state-of-the-art saliency detection methods.

Original languageEnglish (US)
Article number8375812
Pages (from-to)74-85
Number of pages12
JournalIEEE Transactions on Multimedia
Volume21
Issue number1
DOIs
StatePublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Salient Object Detection via Fuzzy Theory and Object-Level Enhancement'. Together they form a unique fingerprint.

  • Cite this