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Quantifying aesthetics of visual des...

Jahanian, Ali.

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Quantifying aesthetics of visual design applied to automatic design.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantifying aesthetics of visual design applied to automatic design./
Author:	Jahanian, Ali.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2014,
Description:	173 p.
Notes:	Source: Dissertations Abstracts International, Volume: 76-11, Section: B.
Contained By:	Dissertations Abstracts International76-11B.
Subject:	Electrical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3688189
ISBN:	9781321663938

Quantifying aesthetics of visual design applied to automatic design.
Jahanian, Ali.

Quantifying aesthetics of visual design applied to automatic design. - Ann Arbor : ProQuest Dissertations & Theses, 2014 - 173 p.

Source: Dissertations Abstracts International, Volume: 76-11, Section: B.

Thesis (Ph.D.)--Purdue University, 2014.

This item must not be sold to any third party vendors.

In today's "Instagram" world, with advances in ubiquitous computing and access to social networks, digital media is adopted by art and culture. In this dissertation, we study what makes a good design by investigating mechanisms to bring aesthetics of design from realm of subjection to objection. These mechanisms are a combination of three main approaches: learning theories and principles of design by collaborating with professional designers, mathematically and statistically modeling good designs from large scale datasets, and crowdscourcing to model perceived aesthetics of designs from general public responses. We then apply the knowledge gained in automatic design creation tools to help non-designers in self-publishing, and designers in inspiration and creativity. Arguably, unlike visual arts where the main goals may be abstract, visual design is conceptualized and created to convey a message and communicate with audiences. Therefore, we develop a semantic design mining framework to automatically link the design elements, layout, color, typography, and photos to linguistic concepts. The inferred semantics are applied to a design expert system to leverage user interactions in order to create personalized designs via recommendation algorithms based on the user's preferences.

ISBN: 9781321663938Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Aesthetics

Quantifying aesthetics of visual design applied to automatic design.
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245 1 0 $a Quantifying aesthetics of visual design applied to automatic design.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2014
300 $a 173 p.
500 $a Source: Dissertations Abstracts International, Volume: 76-11, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Allebach, Jan P.
502 $a Thesis (Ph.D.)--Purdue University, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a In today's "Instagram" world, with advances in ubiquitous computing and access to social networks, digital media is adopted by art and culture. In this dissertation, we study what makes a good design by investigating mechanisms to bring aesthetics of design from realm of subjection to objection. These mechanisms are a combination of three main approaches: learning theories and principles of design by collaborating with professional designers, mathematically and statistically modeling good designs from large scale datasets, and crowdscourcing to model perceived aesthetics of designs from general public responses. We then apply the knowledge gained in automatic design creation tools to help non-designers in self-publishing, and designers in inspiration and creativity. Arguably, unlike visual arts where the main goals may be abstract, visual design is conceptualized and created to convey a message and communicate with audiences. Therefore, we develop a semantic design mining framework to automatically link the design elements, layout, color, typography, and photos to linguistic concepts. The inferred semantics are applied to a design expert system to leverage user interactions in order to create personalized designs via recommendation algorithms based on the user's preferences.
590 $a School code: 0183.
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer engineering. $3 621879
653 $a Aesthetics
653 $a Automatic design
653 $a Color design
653 $a Layout
653 $a Typography
653 $a Visual design
690 $a 0464
690 $a 0544
710 2 $a Purdue University. $b Electrical and Computer Engineering. $3 1018497
773 0 $t Dissertations Abstracts International $g 76-11B.
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791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3688189