ranking buildings and mining the web for popular architectural patterns

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Ranking Buildings and Mining the Web for Popular Architectural Patterns Ujwal Gadiraju, Stefan Dietze and Ernesto Diaz-Aviles Oxford, 29 th June 2015

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Page 1: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Ranking Buildings and Mining the Web for Popular Architectural

Patterns

Ujwal Gadiraju, Stefan Dietze and Ernesto Diaz-Aviles

Oxford, 29th June 2015

Page 2: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Outline

● Motivation

● Background

● Methodology

● Results

● Conclusions

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Page 3: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Camillo Sitte

Main works are “an aesthetic criticism” of 19th century

urbanism. The whole is much more than the

sum of it’s parts.

“City Planning according to artistic principles.” 3

Page 4: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Form follows function VS Ornamentalism

Louis Sullivan

Father of Modernism. Father of Skyscrapers.

“That life is recognizable in its expression,

That form ever follows function. This is the law.”

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Page 5: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Built Environment

Space SyntaxIMPLICATIONS

● Urban planning● Impact of an architectural structure● Identify needs for restructuring,

adequate maintenance and trigger retrofit scenarios

● Predict impact of building projects

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Page 6: Ranking Buildings and Mining the Web for Popular Architectural Patterns

What do people think about buildings?

● (On the way)/(at) home, work, play.● Buildings invoke feelings [1,2].

● Research has established that buildings shape the built environment.

● Built environment influences various aspects within a community.

[1]. Brain electrical responses to high-and low-ranking buildings. Oppenheim et al. Clinical EEG and Neuroscience, 2009.

[2]. Hippocampal contributions to the processing of architectural ranking. Oppenheim et al. NeuroImage, 2010.

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Page 7: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Surveying Experts to establish Influential Factors Building Types

- Skyscrapers

- Bridges

- Churches

- Halls

- Airports

Emerging factors :

● Historic importance

● Effect on/of the surroundings/built environment

● Materials used

● Size of the building/structure

● Personal experiences

● Level of Details

Emerging factors :- Ease of access to airport- Efficiency of movement/processing inside airport- General design & Appearance

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Page 8: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Crowdsourcing Ground Truth

● 5-point Likert Scale (Strongly Dislike - Strongly

Like)● Gold Standards and precautions to

detect and curtail malicious workers or bots [1].

● Images presented with same resolution and dimensions [2].

● Avoid bias by using images from Wikimedia Commons.

● 18,500 trusted responses from 7,396 workers.[1]. Understanding Malicious Behavior on Crowdsourcing Platforms - The Case of Online Surveys.

Ujwal Gadiraju, Ricardo Kawase, Stefan Dietze and Gianluca Demartini. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015.[2]. "Size does matter: how image size affects aesthetic perception?." Chu, Wei-Ta, Yu-Kuang Chen, and Kuan-Ta Chen. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 2013.

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Page 9: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Emerging Influential Factors

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Page 10: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Processing Pipeline for Automated Ranking of Buildings

Crowdsourcing

Web Mining

● News Articles and Blogs

● Tweets

● Meta-data from flickr images (title, description, tags favorites, comments)

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Page 11: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Automated Ranking-Workflow

DatasetCharacteristics

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Page 12: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Models for Ranking Buildings

● Based on perception-related metadata from relevant Flickr images.

● Sentic feature vectors using EmoLex.● RankSVM to learn model(s).● Feature selection for construction of different

models.● Best performing model : Weighted Model

(weighted combination of feature vectors according to influential factors)

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Page 13: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Properties

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Influential Factors

Ground Truth (Crowdsourcing)

Ranking Models

Ranked List

CORRELATE

Well-perceived patterns for Architectural Structures

top-k

Page 14: Ranking Buildings and Mining the Web for Popular Architectural Patterns

DBpedia properties corresponding to Influential Factors

Caveat :

● Coverage of DBpedia properties corresponding to influential factors is limited

SIZE

dbpedia-owl: runwayLengt

h

dbpedia-owl: Length

dbprop: architectureSty

ledbprop:

seatingCapacity

dbpedia: floorCount

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Page 15: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Consolidation of Patterns

CHURCHES: Best-perceived Architectural Styles

● Gothic Revival● Romanesque● Gothic

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Page 16: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Consolidation of Patterns

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Page 17: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Conclusions & Future Work

● Functionalism vs Ornamentalism?● Correlating building rankings with

structured data from the Web can help us to establish popular architectural patterns.

● Building type-specific methods are important.

● Multidimensional architectural patterns through regression of influential factors.

● Using Web Data (both social and structured) in order to fill in the missing gaps.

For example, buildings with x size, y

uniqueness, z materials used, … are best perceived. 17

Page 18: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Summary

● Identified Influential Factors for different building types

● Ground truth construction via Crowdsourcing

● Models for ranking buildings automatically

● Correlated influential factors with structured data from DBpedia

Well-perceived patterns for Architectural Structures

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Page 19: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Contact Details :

[email protected]

http://www.L3S.de

SLIDES: http://www.slideshare.net/ujwal07/

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Page 20: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Influential Factors for Airports

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Page 21: Ranking Buildings and Mining the Web for Popular Architectural Patterns

America’s Favorite Architecture: AIA 150

● 2006-2007 AIA organized a study, carried out by Harris Initiative

● In the first phase : 2,448 AIA members interviewed

● In the second phase: Survey of general public (2,214 people)

● Criticism : o List of favorites did not reflect judgments of

architectural experts o AIA President said, “Rankings reflected

people’s emotional connections to buildings”.

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Page 22: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Popularity vs Perception

● POPULARITY : The state of being liked, admired or being supported by many people.

E.g. Do you KNOW this building?

● PERCEPTION : The way in which something is regarded, understood, or interpreted.

E.g. Do you LIKE this building?

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Page 23: Ranking Buildings and Mining the Web for Popular Architectural Patterns

Plutchik’s Psychoevolutionary Theory of Emotion

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Page 24: Ranking Buildings and Mining the Web for Popular Architectural Patterns

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