Architectural Style Classification Using Multinomial Latent Logistic Regression

  • Zhe Xu
  • Dacheng Tao
  • Ya Zhang
  • Junjie Wu
  • Ah Chung Tsoi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8689)

Abstract

Architectural style classification differs from standard classification tasks due to the rich inter-class relationships between different styles, such as re-interpretation, revival, and territoriality. In this paper, we adopt Deformable Part-based Models (DPM) to capture the morphological characteristics of basic architectural components and propose Multinomial Latent Logistic Regression (MLLR) that introduces the probabilistic analysis and tackles the multi-class problem in latent variable models. Due to the lack of publicly available datasets, we release a new large-scale architectural style dataset containing twenty-five classes. Experimentation on this dataset shows that MLLR in combination with standard global image features, obtains the best classification results. We also present interpretable probabilistic explanations for the results, such as the styles of individual buildings and a style relationship network, to illustrate inter-class relationships.

Keywords

Latent Variable Models Architectural Style Classification Architectural Style Dataset 

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Supplementary material

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zhe Xu
    • 1
    • 2
  • Dacheng Tao
    • 2
  • Ya Zhang
    • 1
  • Junjie Wu
    • 3
  • Ah Chung Tsoi
    • 4
  1. 1.Shanghai Key Laboratory of Multimedia Processing and TransmissionsShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Centre for Quantum Computation & Intelligent Systems and Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia
  3. 3.School of Economics and ManagementBeihang UniversityBeijingChina
  4. 4.Faculty of Information TechnologyMacau University of Science and TechnologyMacauChina

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