A holistic approach for data-driven object cutout

Huayong Xu, Yangyan Li*, Wenzheng Chen, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen

*Corresponding author for this work

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

2 Scopus citations

Abstract

Object cutout is a fundamental operation for image editing and manipulation, yet it is extremely challenging to automate it in real-world images, which typically contain considerable background clutter. In contrast to existing cutout methods, which are based mainly on low-level image analysis, we propose a more holistic approach, which considers the entire shape of the object of interest by leveraging higher-level image analysis and learnt global shape priors. Specifically, we leverage a deep neural network (DNN) trained for objects of a particular class (chairs) for realizing this mechanism. Given a rectangular image region, the DNN outputs a probability map (P-map) that indicates for each pixel inside the rectangle how likely it is to be contained inside an object from the class of interest. We show that the resulting P-maps may be used to evaluate how likely a rectangle proposal is to contain an instance of the class, and further process good proposals to produce an accurate object cutout mask. This amounts to an automatic end-to-end pipeline for catergory-specific object cutout. We evaluate our approach on segmentation benchmark datasets, and show that it significantly outperforms the state-of-the-art on them.

Original languageAmerican English
Title of host publicationComputer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, Revised Selected Papers
EditorsYoichi Sato, Ko Nishino, Vincent Lepetit, Shang-Hong Lai
PublisherSpringer Verlag
Pages245-260
Number of pages16
ISBN (Print)9783319541808
DOIs
StatePublished - 2017
Event13th Asian Conference on Computer Vision, ACCV 2016 - Taipei, Taiwan, Province of China
Duration: 20 Nov 201624 Nov 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10111 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Asian Conference on Computer Vision, ACCV 2016
Country/TerritoryTaiwan, Province of China
City Taipei
Period20/11/1624/11/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

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