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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 Yan

DISI, University o{ Trento, Italy

ADSC, UIUC, Sin}apore

yan@disi.unitn.itG

®ozde®Ozbal

Fondazione Bruno Kessler

Trento, Italy

}ozbalde@}mail.com

Alkim Almila Akda

g Salah

KNAW, e-Humanities Group

Amsterdam, the Netherlands

alelma@ucla.eduAlbert Ali Salah Bo }azic¸i UniversityIstanbul, Tur?ey salah@boun.edu.trNicu Sebe

DISI, University o{ Trento

Trento, Italy

sebe@disi.unitn.it

Abstract

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.

3 Datasets and Ground Truth Collection

We conduct our analysis on two sets of abstract paintings, a professional and an amateur one.

1Both are composed of 500

abstract paintings. In the following subsections we detail the selection process as well as the nature of each dataset.

3.1 MART Dataset: A Dataset o{ Pro{essional

Abstract Paintin}s

The MART dataset is a set collected in our previous work [Yanulevskayaet al., 2012; Sartoriet al., 2015]from the electronic archive of the Museum of Modern and Con- temporary Art of Trento and Rovereto (MART). The selected paintings are masterpieces of abstract art, which were cre- ated by a total of 78 artists between 1913 and 2008. Most of these artists are Italians, however there are also European and American artists. Some of the artists, such as Wassily Kandinsky, Josef Albers, Paul Klee and Luigi Veronesi, are known for their studies on abstract art in terms of colour, shapes and texture. As most of the artists are from Italy, the titles are in Italian. The available descriptions or the state- ments of the artists about these paintings are also in Italian.

3.? deviantArt Dataset: A Dataset o{ Amateur

Abstract Paintin}s

The amateur collection of abstract paintings has been col- lected from the deviantArt (dA) website

2, an online social

network dedicated to user-generated art. This set was col- lected in our previous work [Sartoriet al., 2015]. dA is one of the largest online art communities with more than

280 million artworks and 30 million registered users. We se-

lected the artworks that were under the category Traditional1 The datasets with their respective ground truths are publicly available at: http://disi.unitn.it/ sartori/datasets/

2http://www.deviantart.com/2504

Art/Paintings/Abstract, and downloaded 8,000 artworks. We used the information reecting how many times an artwork was favorited as a parameter to downsize the collection from

8000 to 500 paintings

3. We selected the paintings with the

highest and least number of favourites, as well as some paint- ings randomly in the middle. The collection thus has 500 paintings with 406 different authors. The titles and the artist descriptions in this collection are in English.

3.3 User Study to Analyse Emotions Evoked by

Artworks

To collect our ground truth we use the relative score method from our previous work [Sartoriet al., 2015]in which we asked people to choose the more positive painting in a pair. The annotation was done online and we provided the follow- inginstructionstoeachannotator: ìWhichpaintinginthepair looks more positive to you? Let your instinct guide you and follow your rst impression of the paintings.î To annotate and calculate the emotional scores from the paintings we follow the method of TrueSkill ranking sys- tem [Herbrich and Graepel, 2006; Moser, 2010]. Developed by Microsoft Research for Xbox Live, the TrueSkill rank- ing system recognizes and ranks the skills of the players in a game and matches players with similar skills for a new game. With this method the annotation task is more man- ageable, as it yields a representative annotation with only

3,750 pairs of paintings, instead of 124,750 comparisons

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