Deep model style: Cross-class style compatibility for 3D furniture within a scene

Tse Yu Pan, Yi Zhu Dai, Wan Lun Tsai, Min Chun Hu

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

Harmonizing the style of all the furniture placed within a constrained space/scene has been regarded as one of the most important tasks in interior design. Most previous style analysis works measure the style similarity or compatibility of the objects based on predefined geometric features extracted from 3D models. However, 'style' is a high-level semantic concept, which is difficult to be described explicitly by handcrafted geometric features. Deep neural network has been claimed to have more powerful ability to mimic the perception of human visual cortex. Therefore, in this work we utilize Triplet Convolutional Neural Network (Triplet CNN) to analyze style compatibility between 3D furniture models of different classes (e.g., a table and a lamp). It should be noted that analyzing the style compatibility between two or more furniture of different classes is quite difficult, as the given furniture may have distinctive structures or geometric elements. We conducted experiments based on a collected dataset containing 420 textured 3D furniture models. A group of raters were recruited from Amazon Mechanical Turk (AMT) to evaluate the comparative suitability of paired models within the dataset. The experimental results reveal that the proposed furniture style compatibility method based on deep learning is better than the state-of-the-art method and can be used for furniture recommendation.

原文English
主出版物標題Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
編輯Jian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
發行者Institute of Electrical and Electronics Engineers Inc.
頁面4307-4313
頁數7
ISBN(電子)9781538627143
DOIs
出版狀態Published - 2017 七月 1
事件5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
持續時間: 2017 十二月 112017 十二月 14

出版系列

名字Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
國家/地區United States
城市Boston
期間17-12-1117-12-14

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信
  • 硬體和架構
  • 資訊系統
  • 資訊系統與管理
  • 控制和優化

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