Linking Past to Present: Discovering Style in Two Centuries of Architecture

Maisonneuve, Nicolas; Crandall, David J.; Efros, Alexei A.; Šivic, Josef

doi:10.1109/iccphot.2015.7168368

Cited by 42 publications

(39 citation statements)

References 17 publications

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“…A limitation of the works in [7] and [12] is that the patch matching is based on HOG descriptors. Initial experiments with our data Figure 2: Overview of our approach for building age estimation.…”

Section: Related Workmentioning

confidence: 99%

Automatic Prediction of Building Age from Photographs

Zeppelzauer

Despotović

Sakeena

et al. 2018

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval

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We present a first method for the automated age estimation of buildings from unconstrained photographs. To this end, we propose a two-stage approach that firstly learns characteristic visual patterns for different building epochs at patch-level and then globally aggregates patch-level age estimates over the building. We compile evaluation datasets from different sources and perform an detailed evaluation of our approach, its sensitivity to parameters, and the capabilities of the employed deep networks to learn characteristic visual age-related patterns. Results show that our approach is able to estimate building age at a surprisingly high level that even outperforms human evaluators and thereby sets a new performance baseline. This work represents a first step towards the automated assessment of building parameters for automated price prediction. CCS CONCEPTS• Information systems → Information retrieval; • Computing methodologies → Visual content-based indexing and retrieval; Supervised learning; Neural networks; KEYWORDS Content-based image retrieval, visual pattern extraction, image classification, building analysis, building age estimation, deep learning.

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Section: Related Workmentioning

confidence: 99%

Automatic Prediction of Building Age from Photographs

Zeppelzauer

Despotović

Sakeena

et al. 2018

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval

View full text Add to dashboard Cite

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“…We tune λ through different settings of learning rate η (1) and η (2) in Eq. (8) and (9). The initial learning rate η (1) in patch optimization is 0.02.…”

Section: Discussion Of Our Methodsmentioning

confidence: 99%

“…Most existing style classification methods mainly focused on extracting discriminative local patches or patterns from low-level features [9,18,20,21,10,5]. However, none of these style classification works has discussed the spread out phenomenon-an important observation of style images.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Style classification, such as the architectural style [8,9], the manga style [10] and the fashion style [5] attracts increasing attention from researchers in vision and machine learning fields.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Style classification such as the architectural style [17,8,9,18], the music style [19,20,21], the photographic style [22], the manga style [10] and the fashion style [5,23,24,25] attracts increasing attention recently. Fashion style classification essentially ties to the clothing parsing [3], clothing recognition [11] and fashion style generation [7].…”

Section: Fashion Style Classification Retrieval and Generationmentioning

confidence: 99%

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This paper applies state-of-the-art techniques in deep learning and computer vision to measure visual similarities between architectural designs by different architects. Using a dataset consisting of web scraped images and an original collection of images of architectural works, we first train a deep convolutional neural network (DCNN) model capable of achieving 73% accuracy in classifying works belonging to 34 different architects. Through examining the weights in the trained DCNN model, we are able to quantitatively measure the visual similarities between architects that are implicitly learned by our model. Using this measure, we cluster architects that are identified to be similar and compare our findings to conventional classification made by architectural historians and theorists. Our clustering of architectural designs remarkably corroborates conventional views in architectural history, and the learned architectural features also coheres with the traditional understanding of architectural designs.

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Linking Past to Present: Discovering Style in Two Centuries of Architecture

Cited by 42 publications

References 17 publications

Automatic Prediction of Building Age from Photographs

Automatic Prediction of Building Age from Photographs

Regularized deep learning for fashion and social media data analysis

Deep Learning Architect: Classification for Architectural Design Through the Eye of Artificial Intelligence

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