Room style estimation for style-Aware recommendation

Esra Ataer-Cansizoglu, Hantian Liu, Tomer Weiss, Archi Mitra, Dhaval Dholakia, Jae Woo Choi, Dan Wulin

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

8 Scopus citations

Abstract

Interior design is a complex task as evident by multitude of professionals, websites, and books, offering design advice. Additionally, such advice is highly subjective in nature since different experts might have different interior design opinions. Our goal is to offer data-driven recommendations for an interior design task that reflects an individual's room style preferences. We present a style-based image suggestion framework to search for room ideas and relevant products for a given query image. We train a deep neural network classifier by focusing on high volume classes with high-Agreement samples using a VGG architecture. The resulting model shows promising results and paves the way to style-Aware product recommendation in virtual reality platforms for 3D room design.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages267-270
Number of pages4
ISBN (Electronic)9781728156040
DOIs
StatePublished - Dec 2019
Externally publishedYes
Event2nd IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2019 - San Diego, United States
Duration: Dec 9 2019Dec 11 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2019

Conference

Conference2nd IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2019
Country/TerritoryUnited States
CitySan Diego
Period12/9/1912/11/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Media Technology
  • Modeling and Simulation

Keywords

  • Neural networks
  • Recommendation
  • Style estimation

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