Piet Mondrian Victory Boogie Woogie 1942-44
A 2*161)4#2* 1( 1942 5*195 6*’ #46+56 #;+0) 176 Victory Boogie Woogie (VBW) +0 106+07175, 70+(14/ +0’5 6*#6 *’ 24’57/#$ ; 6*’0 &+8+&’& 61 (14/ # 8#4
Looking at Mondrians Victory Boogie-Woogie: What Do I Feel?
a painting may induce The Boogie-Woogie was a cultural move-ment of music and dance in the late 1920s, and it is characterized by its vivacity, syncopated beat and irreverent approach to melody This movement fascinated Mondrian as he considered it similar to his own work: “destruction of natural appearance; and construc-
A program for Victory Boogie Woogie
A program for Victory Boogie Woogie Citation for published version (APA): Feijs, L M G (2019) A program for Victory Boogie Woogie Journal of Mathematics and the Arts, 13(3), 261-
Broadway Boogie Woogie Victory Boogie Woogie , created in the
blocks in Broadway Boogie Woogie (1942-1943) and Victory Boogie Woogie , created in the following year, shows that rhythm is an important element in the depiction of jazz music In regard to color, Henri Matisse’s chromatic improvisations in his famous cut-out work, Jazz (1947), show the importance of color to the inimitable nature of jazz music
02/12 - e-flux
Victory Boogie Woogie (1944) and its predecessor piece, Broadway Boogie Woogie (1943), in his own book on the Dutch painter, touching non some of the most compelling aspects of the work: While the space [of Victory Boogie Woogie] is nevertheless very dynamic (not least because of the lozenge format), its dynamism is the result of a virtually
architectonics and abstraction - TTU
Victory Boogie Woogie not so final victory photograph of Mondrianʼs studio on 59th street, New York showing Victory Boogie-Woogie en point on his easel
Lijnen en primaire kleuren - Startpagina
Victory Boogie Woogie Twintig jaar geleden kwam er een beroemd schilderij in 5 Nederlandse handen Het was Victory Boogie Woogie van de Nederlandse schilder Piet Mondriaan, die leefde van 1872 tot 1944 De aankoop van dit werk voor 37 miljoen euro van een Amerikaanse eigenaar werd destijds mogelijk gemaakt door
Coppernickel Goes Mondrian
• Show images and titles of Piet Mondrian’s work, Broadway Boogie-Woogie, Victory Boogie-Woogie and Composition A Allow students time to discuss • Mondrian was inspired by rhythm and music, especially jazz Show Visual 1 • Ask students to determine if image is symmetrical or asymmetrical How many rectangles are in the top row?
[PDF] rapport de stage renault dacia maroc
[PDF] piet mondrian composition with large blue plane
[PDF] red
[PDF] black
[PDF] yellow
[PDF] rapport de stage renault casablanca
[PDF] and gray
[PDF] rapport de stage hyundai pdf
[PDF] rapport de stage graphiste original
[PDF] mondrian arbre
[PDF] rapport de stage agence de communication evenementielle
[PDF] mondrian composition en rouge jaune bleu et noir
[PDF] rapport de stage agence de communication pdf
[PDF] problématique rapport de stage agence de communication
[PDF] mondrian oeuvres célèbres
Looking at Mondrian's Victory Boogie-Woogie: What Do I Feel?
[PDF] piet mondrian composition with large blue plane
[PDF] red
[PDF] black
[PDF] yellow
[PDF] rapport de stage renault casablanca
[PDF] and gray
[PDF] rapport de stage hyundai pdf
[PDF] rapport de stage graphiste original
[PDF] mondrian arbre
[PDF] rapport de stage agence de communication evenementielle
[PDF] mondrian composition en rouge jaune bleu et noir
[PDF] rapport de stage agence de communication pdf
[PDF] problématique rapport de stage agence de communication
[PDF] mondrian oeuvres célèbres
Looking at Mondrian's Victory Boogie-Woogie: What Do I Feel?
Andreza Sartori
DISI, University o{ Trento
Telecom Italia - SKIL Lab
Trento, Italy
andreza.sartori@disi.unitn.itYan YanDISI, University o{ Trento, Italy
ADSC, UIUC, Sin}apore
yan@disi.unitn.itG®ozde®Ozbal
Fondazione Bruno Kessler
Trento, Italy
}ozbalde@}mail.comAlkim Almila Akda
g SalahKNAW, e-Humanities Group
Amsterdam, the Netherlands
alelma@ucla.eduAlbert Ali Salah Bo }azic¸i UniversityIstanbul, Tur?ey salah@boun.edu.trNicu SebeDISI, University o{ Trento
Trento, Italy
sebe@disi.unitn.itAbstract
Abstract artists use non-}urative elements (i.e.
colours, lines, shapes, and textures) to convey emo- tions and o{ten rely on the titles o{ their various reaction in the audience. Several psycholo}ical wor?s observed that the metadata (i.e., titles, de- scription and/or artist statements) associated with paintin}s increase the understandin} and the aes- thetic appreciation o{ artwor?s. In this paper we ex- plore i{ the same metadata could {acilitate the com- putational analysis o{ artwor?s, and reveal what ?ind o{ emotional responses they awa?e. To this end, we employ computer vision and sentiment analysis to learn statistical patterns associated with positive and ne}ative emotions on abstract paint- in}s. We propose a multimodal approach which combines both visual and metadata {eatures in or- der to improve the machine per{ormance. In partic- ular, we propose a novel joint ?exible Schattenp- norm model which can exploit the sharin} patterns between visual and textual in{ormation {or abstract paintin} emotion analysis. Moreover, we conduct a qualitative analysis on the cases in which metadata help improvin} the machine per{ormance.1 Introduction
Throu}hout the centuries, Western Art was dominated by rep- resentational artwor?s, which were power{ul tools to express The title aims to provide an idea about the {eelin} a title o{ a paintin} may induce. The Boo}ie-Woo}ie was a cultural move- ment o{ music and dance in the late 19?0s, and it is characterized by its vivacity, syncopated beat and irreverent approach to melody. This movement {ascinated Mondrian as he considered it similar to his own wor?: destruction o{ natural appearance; and construc- tion throu}h continuous opposition o{ pure means dynamic rhythm." [The Museum o{ Modern Art, ?015]{eelin}s and ideas o{ artists. With the introduction o{ mod- ern art movements li?e abstract art, this emphasis shi{ted and artists explored di{{erent means to evo?e emotions. For ex- ample, abstract artists used the relationship between colour, shapes and textures to convey emotions in a non-}urative" way: artists sou}ht to express in their wor? only internal truths, renouncin} in consequence all consideration o{ exter- nal {orm" [Kandins?y, 1914]. It is there{ore intri}uin} to see i{ we have the same {eelin}s when we loo? at these paintin}s, and i{ we can train a computer al}orithm to report the same. The relationship between paintin}s, emotions and art ap- preciation has been extensively studied in many elds. Psy- cholo}ical studies on aesthetics and emotional responses to art have shown that titles and descriptions in?uence the per- ceptual experience o{ paintin}s. These wor?s demonstrated that people describe paintin}s di{{erently a{ter they read the title [Fran?linet al., 1993]. They also postulate that title and in} o{ the artwor? [Lederet al., ?006]and the aesthetic eval- uation [Millis, ?001; Hristovaet al., ?011]. The in?uence o{ title and description is even more crucial {or abstract artwor?s where the visual clues are open to interpretation. In this paper, we analyze the in?uence o{ the metadata (i.e., titles, description and/or artist's statement) associated with the abstract paintin} and investi}ate how it can be a si}ni{- icant {eature to the automatic emotion reco}nition o{ abstract paintin}s. Specically, we extract the correspondin} meta- data {or the paintin}s and apply sentiment analysis to detect the emotional meanin} o{ these texts. We use state-o{-the- art computer vision techniques employin} colour, shapes and textures to au}ment computation o{ the emotional in{orma- tion o{ the artwor?. Finally, we propose two approaches to combine textual {eatures with visual ones: the rst approach is based on wei}hted linear combination, and the second, we propose a novel joint ?exible Schattenp-norm model. We apply our multimodal approach on two datasets o{ abstract paintin}s: (1) a collection o{ pro{essional paintin}s {rom the MART Museum and (?) a collection o{ amateur paintin}s{rom deviantArt (dA), an online social networ? site desi}-Proceedings of the Twenty-Fourth International Joint Conference on Artif
icial Intelligence (IJCAI 2015)2503 nated to user-generated art. To summarize, our main contributions are: (1) we study the contribution of metadata on positive and negative feelings induced by abstract paintings; (2) we propose a novel joint exible Schattenp-norm model which can exploit the shar- ing patterns between visual and textual information for emo- tion analysis of paintings; (3) we apply our approach on two different datasets of abstract artworks and make a qualitative analysis. This study will contribute to various disciplines and to psychology and aesthetics. ? Related Wor? Several psychological studies provide empirical analysis of how the titles and description of artworks can affect the per- ception and understanding of paintings. Franklin et al. [1993] showed to the subjects the same painting with two different titles (i.e., the original title and a fabricated title), and asked them to describe the painting. They observed that the change of titles affected the interpretation of artworks. Millis [2001] compared the effects of metaphorical (or elaborative) versus descriptive titles on the understanding and the enjoyment of artworks. He found out that metaphorical titles increase the aesthetic experience more than descriptive titles, or no-titles.In a similar study, Leder et al.
[2006]showed that elaborative titles increase the understanding of abstract paintings, but do not affect their appreciation. Hristova et al. [2011]analysed how the style of the paintings and the titles can inuence the xationdurationandsaccadeamplitudeoftheviewers. When the viewers are presented with the title of a painting while looking at the artwork, the average number of saccades of each viewer to observe the work tend to decline. This nding draws the conclusion that titles lead to a more focal process- ing of the paintings. The above mentioned research demon- strate that the metadata inuence the perception of artworks. Recently there is an increase on the research focusing on emotions in artworks from the computational perspective.Yanulevskaya et al.
[2008]proposed an emotion categoriza- tion system for masterpieces based on the assessment of lo- cal image statistics, applying supervised learning of emo- tion categories using Support Vector Machines. Their sys- tem was trained on the International Affective Picture System (IAPS), which is a standard emotion evoking image set [Lang et al. , 1999 ], and was applied to a collection of masterpieces.Machajdik & Hanbury
[2010]employed low-level features and combined with concepts from psychology and art the- ory to categorize images and artworks emotionally. They ob- tained better accuracy in affective categorization of seman- tically rich images than abstract paintings. Yanulevskayaet al. [2012]trained a Bag-of-Visual-Words model to classify abstract paintings in positive or negative emotions. With the backprojection technique the authors determined which parts of the paintings evoke positive or negative emotions. Re- cently, Zhao et al. [2014]extracted emotion features of im- ages from IAPS dataset of [Langet al., 1999]based on the principles of art, including balance, emphasis, harmony, vari- ety, gradation, and movement. They concluded that these fea-tures can improve the performance of emotion recognition inimages. Inthiswork, weusemetadataasanadditionalfeatureto improve the affective classication of abstract paintings.
Various computational approaches have shown the impor- tance of detecting emotion in textual information [Strappa- rava and Mihalcea, 2008; Balahuret al., 2011]. Sentiment analysis techniques using computational linguistics to extract the human sentiment in text, is increasingly used in many ar- eas, such as marketing and social networks to improve their products and/or services. Recent works, including [Hwang and Grauman, 2012; Gonget al., 2014], empathize the im- portance of using textual information associated with images for creating stronger models for image classication. Liu et al. [2011]collected textual information from Internet in the form of tags describing images. They applied two differ- ent methods to extract emotional meaning from these tags and combined them with visual features. This study demon- strated how textual information associated with images can improve affective image classication. In our work, we use a multimodal approach to investigate how the title, descriptions stronger model to perform affective analysis of abstract paint- ings.