Architectural Style Classification of Building Facade Windows

Shalunts, Gayane; Haxhimusa, Yll; Sablatnig, Robert

doi:10.1007/978-3-642-24031-7_28

Cited by 46 publications

(24 citation statements)

References 15 publications

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“…Beyond this, methods for the analysis and mining of different architectural styles have been introduced. A first approach to classify old buildings into different styles (Gothic, Baroque, and Romanesque) has been introduced by [24]. The authors extract SIFT features and Bag of Visual Words (BoVW) histograms to predict different architectural styles.…”

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

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“…In addition, space is experienced not only through our perceptions but also through our other senses. In terms of techniques, most previous literature employs clustering and learning of local features (Shalunts et al, 2011), but not deep learning (Llamas et al, 2017). This paper attempts to classify designs of modern and contemporary architecture using a deep convolutional neural network.…”

Section: Related Workmentioning

confidence: 99%

“…From a technical point of view, our contribution includes the application of DCNN to the design style classification, because it is a different topic from the element-based classification. The former is well researched, including the field of architecture (Shalunts et al, 2011), while most of the latter is researched in the fields of art (Tan et al, 2016). For this purpose, we employ recently developed deep learning techniques in processing the visual images to classify the given datasets through the training samples.…”

Section: Introductionmentioning

confidence: 99%

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

Yoshimura

Cai

Wang³

et al. 2019

Lecture Notes in Geoinformation and Cartography

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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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“…(Doersch, Singh et al 2012). Related to that, there is also a research line into the automatic classification of architectural styles by capturing the morphological characteristics, which can be further applied to the identification of architectural style mix and style transformation over time (Shalunts, Haxhimusa et al 2011;Goel, Juneja et al 2012;Shalunts, Haxhimusa et al 2012;Xu, Tao et al 2014;Lee, Maisonneuve et al 2015).…”

Section: Related Workmentioning

confidence: 99%

A machine learning-based method for the large-scale evaluation of the qualities of the urban environment

Liu

Silva

et al. 2017

Computers, Environment and Urban Systems

227

107

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Given the size of modern cities in the urbanising age, it is beyond the perceptual capacity of most people to develop a good knowledge about the beauty and ugliness of the city at every street corner. Correspondingly, for planners, it is also difficult to accurately answer questions like 'where are the worst-looking places in the city that regeneration should give first consideration', or 'in the fast urbanising cities, how is the city appearance changing', etc. To address this issue, we here present a computer vision method for the large-scale and automatic evaluation of the urban visual environment, by leveraging state-of-the-art machine learning techniques and the wide-coverage street view images. From the various factors that are at work, we choose two key features, the visual quality of street façade and the continuity of street wall, as the starting point of this line of analysis. In order to test the validity of this method, we further compare the machine ratings with ratings collected on site from 752 passers-by on fifty-six locations. We show that the machine learning model can produce a good estimation of people's real visual experience, and it holds much potential for various tasks in terms of urban design evaluation, culture identification, etc.

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Architectural Style Classification of Building Facade Windows

Cited by 46 publications

References 15 publications

Automatic Prediction of Building Age from Photographs

Automatic Prediction of Building Age from Photographs

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

A machine learning-based method for the large-scale evaluation of the qualities of the urban environment

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