COMPUTATIONAL MODELLING OF HUMAN AESTHETIC PREFERENCES IN THE VISUAL DOMAIN: A BRAIN-INSPIRED APPROACH

Following the rise of neuroaesthetics as a research domain, computational aesthetics has also known a regain in popularity over the past decade with many works using novel computer vision and machine learning techniques to evaluate the aesthetic value of visual information. This thesis presents a new approach where low-level features inspired from the human visual system are extracted from images to train a machine learning-based system to classify visual information depending on its aesthetics, regardless of the type of visual media. Extensive tests are developed to highlight strengths and weaknesses of such low-level features while establishing good practices in the domain of study of computational aesthetics. The aesthetic classification system is not only tested on the most widely used dataset of photographs, called AVA, on which it is trained initially, but also on other photographic datasets to evaluate the robustness of the learnt aesthetic preferences over other rating communities. The system is then assessed in terms of aesthetic classification on other types of visual media to investigate whether the learnt aesthetic preferences represent photography rules or more general aesthetic rules. The skill transfer from aesthetic classification of photos to videos demonstrates a satisfying correct classification rate of videos without any prior training on the test set created by Tzelepis et al. Moreover, the initial photograph classifier can also be used on feature films to investigate the classifier’s learnt visual preferences, due to films providing a large number of frames easily labellable. The study on aesthetic classification of videos concludes with a case study on the work by an online content creator. The classifier recognised a significantly greater percentage of aesthetically high frames in videos filmed in studios than on-the-go. The results obtained across datasets containing videos of diverse natures manifest the extent of the system’s aesthetic knowledge. To conclude, the evolution of low-level visual features is studied in popular culture such as in paintings and brand logos. The work attempts to link aesthetic preferences during contemplation tasks such as aesthetic rating of photographs with preferred low-level visual features in art creation. It questions whether favoured visual features usage varies over the life of a painter, implicitly showing a relationship with artistic expertise. Findings display significant changes in use of universally preferred features over influential vi abstract painters’ careers such an increase in cardinal lines and the colour blue; changes that were not observed in landscape painters. Regarding brand logos, only a few features evolved in a significant manner, most of them being colour-related features. Despite the incredible amount of data available online, phenomena developing over an entire life are still complicated to study. These computational experiments show that simple approaches focusing on the fundamentals instead of high-level measures allow to analyse artists’ visual preferences, as well as extract a community’s visual preferences from photos or videos while limiting impact from cultural and personal experiences

Analysing formal visual elements of corporate logotypes using computational aesthetics

The marketing mix contains a significant proportion of elements that derive their appeal and effectiveness from visuals. This thesis proposes the application of quantitative measures from the literature on computational aesthetics to evaluate and study the formal characteristics of corporate visuals in the form of logotypes (logos). It is argued that the proposed approach has a number of advantages in terms of efficiency, consistency and accuracy over existing approaches in marketing that rely on subjective assessments. The proposed approach is grounded on a critical review of a diverse literature that encompasses Marketing, Art History and Philosophy, and, Visual Science and Psychology. The computational aesthetic measures are framed within the construct of Henderson and Cote (1998) and van der Lans et al. (2009), in order to analyse brand logo design elements along with their effect on consumers. The thesis is underpinned by three empirical studies. The first study uses an extensive set of 107 computational aesthetic measures to quantify the design elements in a sample of 215 professionally designed logotypes drawn from the World Intellectual Property Organization Global Brand Database. The study uses for the first time an array of different measures for evaluating design elements related to colour that include hue, saturation, and colourfulness. The metrics capture both global design features of logos along with features related to visual segments. The metrics are linked to logo elaborateness, naturalness and harmony, using the theoretical framework of Henderson and Cote (1998). The results show that measures have a very diverse behaviour across metrics and typically follow highly non-normal distributions. Factor analysis indicates that the categorisation of the measurements in three factors is a reasonable representation of the data with some correspondence to the dimensions of elaborateness, naturalness and harmony. The second study demonstrates that the proposed computational aesthetic measures can be used to approximate the subjective evaluation of logo designs provided by experts.   Specifically, eight design elements for the sample of 215 logos, corresponding to harmony, elaborateness and naturalness, are evaluated by three experts. The results show for the first time that computational aesthetic measures related to colour along with other measures are useful in approximating subjective expert reviews. Unlike previous literature, this research combines both standard statistical methods for modelling and inference, along with more recent techniques from machine learning. Linear regression analysis suggests that the objective computational measures contain useful information for predicting proxy subjective expert reviews for logos. Model accuracy is substantially improved using neural network regression analysis based on Radial Basis Functions. The last study examines the role of consumer personality traits as moderators of the effect of perceived logo dynamism on consumer attitude towards the logo. One hundred and twenty-two participants were asked to evaluate elements of logo design (visual appearance, complexity, informativeness, familiarity, novelty, dynamism and engagement), their attitude towards the brand and their personality traits (sensation seeking, risk taking propensity, nostalgia and need for cognition). The estimates extracted were shown to vary significantly in terms of central tendency and dispersion and mostly follow non-normal distributions. Following Cian et al. (2014) the moderated mediator model by Preacher and Hayes (2008) is applied to test the suitability of personality traits as moderators of the effect of logo dynamism on attitudes towards the logo. The personality traits used as moderators are Need for Cognition and Risk-Taking Propensity, whereas Engagement was used as a Mediator. This is the first study to employ personality traits as moderators in such a study using this methodology. The results offer limited support of the role of personality traits as moderators in this relationship. Therefore, the study strengthens the case for the development of objective measures of visual characteristics. The working hypothesis in the thesis is that, with the help of computational aesthetic measures, marketing visuals such as corporate logos, can afford themselves to a consistent quantitative approach which can prove to be important for researchers and practitioners alike. By being able to group and measure the aesthetic differences, similarities and emerging patterns, access is gained to a new family of metrics, which can be applied to any type of logo across time, product, industry or culture

Colorful textile antennas integrated into embroidered logos

We present a new methodology to create colorful textile antennas that can be embroidered within logos or other aesthetic shapes. Conductive threads (e-Threads) have already been used in former embroidery unicolor approaches as attributed to the corresponding conductive material, viz. silver or copper. But so far, they have not been adapted to \u27print\u27 colorful textile antennas. For the first time, we propose an approach to create colorful electronic textile shapes. In brief, the embroidery process uses an e-Thread in the bobbin case of the sewing machine to embroider the antenna on the back side of the garment. Concurrently, a colorful assistant yarn is threaded through the embroidery needle of the embroidery machine and used to secure or \u27couch\u27 the e-Threads onto the fabric. In doing so, a colorful shape is generated on the front side of the garment. The proposed antennas can be unobtrusively integrated into clothing or other accessories for a wide range of applications (e.g., wireless communications, Radio Frequency IDentification, sensing)

Digital aesthetics: the discrete and the continuous

Aesthetic investigations of computation are stuck in an impasse, caused by the difficulty of accounting for the ontological discrepancy between the continuity of sensation and the discreteness of digital technology. This article proposes a theoretical position intended to overcome that deadlock. It highlights how an ontological focus on continuity has entered media studies via readings of Deleuze, which attempt to build a ‘digital aisthesis’ (that is, a theory of digital sensation) by ascribing a ‘virtuality’ to computation. This underpins, in part, the affective turn in digital theory. In contrast to such positions, this article argues for a reconceptualization of formal abstraction in computation, in order to find, within the discreteness of computational formalisms (and not via the coupling of the latter with virtual sensation), an indeterminacy that would make computing aesthetic qua inherently generative. This indeterminacy, it is argued here, can be found by reconsidering, philosophically, Turing’s notion of ‘incomputability’

Computer Analysis of Architecture Using Automatic Image Understanding

In the past few years, computer vision and pattern recognition systems have been becoming increasingly more powerful, expanding the range of automatic tasks enabled by machine vision. Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion. Images of buildings from 18 cities and three countries were acquired using Google StreetView, and were used to train a machine vision system to automatically identify the location of the imaged building based on the image visual content. Experimental results show that the automatic computer analysis can automatically identify the geographical location of the StreetView image. More importantly, the algorithm was able to group the cities and countries and provide a phylogeny of the similarities between architectural styles as captured by StreetView images. These results demonstrate that computer vision and pattern recognition algorithms can perform the complex cognitive task of analyzing images of buildings, and can be used to measure and quantify visual similarities and differences between different styles of architectures. This experiment provides a new paradigm for studying architecture, based on a quantitative approach that can enhance the traditional manual observation and analysis. The source code used for the analysis is open and publicly available