A shape-based approach for salient object detection using deep learning

Jongpil Kim, Vladimir Pavlovic

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

30 Scopus citations

Abstract

Salient object detection is a key step in many image analysis tasks as it not only identifies relevant parts of a visual scene but may also reduce computational complexity by filtering out irrelevant segments of the scene. In this paper, we propose a novel salient object detection method that combines a shape prediction driven by a convolutional neural network with the mid and low-region preserving image information. Our model learns a shape of a salient object using a CNN model for a target region and estimates the full but coarse saliency map of the target image. The map is then refined using image specific low-to-mid level information. Experimental results show that the proposed method outperforms previous state-of-the-arts methods in salient object detection.

Original languageEnglish (US)
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsNicu Sebe, Bastian Leibe, Max Welling, Jiri Matas
PublisherSpringer Verlag
Pages455-470
Number of pages16
ISBN (Print)9783319464923
DOIs
StatePublished - Jan 1 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9908 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Convolutional neural networks
  • Deep learning
  • Salient object detection

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