Ali Jahanian

Automatic design of magazine covers

In this paper, we propose a system for automatic design of magazine covers that quantifies a number of concepts from art and aesthetics. Our solution to automatic design of this type of media has been shaped by input from professional designers, magazine art directors and editorial boards, and journalists. Consequently, a number of principles in design and rules in designing magazine covers are delineated. Several techniques are derived and employed in order to quantify and implement these principles and rules in the format of a software framework. At this stage, our framework divides the task of design into three main modules: layout of magazine cover elements, choice of color for masthead and cover lines, and typography of cover lines. Feedback from professional designers on our designs suggests that our results are congruent with their intuition.

Colors -- Messengers of Concepts: Visual Design Mining for Learning Color Semantics

We study the concept of color semantics by modeling a dataset of magazine cover designs, evaluating the model via crowdsourcing, and demonstrating several prototypes that facilitate color-related design tasks. We investigate a probabilistic generative modeling framework that expresses semantic concepts as a combination of color and word distributions -- color-word topics. We adopt an extension to Latent Dirichlet Allocation (LDA) topic modeling, called LDA-dual, to infer a set of color-word topics over a corpus of 2,654 magazine covers spanning 71 distinct titles and 12 genres. Although LDA models text documents as distributions over word topics, we model magazine covers as distributions over color-word topics. The results of our crowdsourcing experiments confirm that the model is able to successfully discover the associations between colors and linguistic concepts. Finally, we demonstrate several prototype applications that use the learned model to enable more meaningful interactions in color palette recommendation, design example retrieval, pattern recoloring, image retrieval, and image color selection.

Automatic design of colors for magazine covers

In the design of a magazine cover, making a set of decisions regarding the color distribution of the cover image and the colors of other graphical and textual elements is considered to be the concept of color design. This concept addresses a number of subjective challenges, specifically how to determine a set of colors that is aesthetically pleasing yet also contributes to the functionality of the design, the legibility of textual elements, and the stylistic consistency of the class of magazine. Our solution to automatic color design includes the quantification of these challenges by deploying a number of well-known color theories. These color theories span both color harmony and color semantics. The former includes a set of geometric structures that suggest which colors are in harmony together. The latter suggests a higher level of abstraction. Color semantics means to bridge sets of color combinations with color mood descriptors. For automatic design, we aim to deploy these two viewpoints by applying geometric structures for the design of text color and color semantics for the selection of cover images.

Learning visual balance from large-scale datasets of aesthetically highly rated images

The concept of visual balance is innate for humans, and influences how we perceive visual aesthetics and cognize harmony. Although visual balance is a vital principle of design and taught in schools of designs, it is barely quantified. On the other hand, with emergence of automantic/semi-automatic visual designs for self-publishing, learning visual balance and computationally modeling it, may escalate aesthetics of such designs. In this paper, we present how questing for understanding visual balance inspired us to revisit one of the well-known theories in visual arts, the so called theory of “visual rightness”, elucidated by Arnheim. We define Arnheim’s hypothesis as a design mining problem with the goal of learning visual balance from work of professionals. We collected a dataset of 120K images that are aesthetically highly rated, from a professional photography website. We then computed factors that contribute to visual balance based on the notion of visual saliency. We fitted a mixture of Gaussians to the saliency maps of the images, and obtained the hotspots of the images. Our inferred Gaussians align with Arnheim’s hotspots, and confirm his theory. Moreover, the results support the viability of the center of mass, symmetry, as well as the Rule of Thirds in our dataset.

Autonomous Color Theme Extraction From Images Using Saliency

Color theme (palette) is a collection of color swatches for representing or describing colors in a visual design or an image. Color palettes have broad applications such as serving as means in automatic/semi-automatic design of visual media, as measures in quantifying aesthetics of visual design, and as metrics in image retrieval, image enhancement, and color semantics. In this paper, we suggest an autonomous mechanism for extracting color palettes from an image. Our method is simple and fast, and it works on the notion of visual saliency. By using visual saliency, we extract the fine colors appearing in the foreground along with the various colors in the background regions of an image. Our method accounts for defining different numbers of colors in the palette as well as presenting the proportion of each color according to its visual conspicuity in a given image. This flexibility supports an interactive color palette which may facilitate the designer’s color design task. As an application, we present how our extracted color palettes can be utilized as a color similarity metric to enhance the current color semantic based image retrieval techniques.

Mining Visual Evolution in 21 Years of Web Design

The web contains a treasure trove of design data, with many web pages being the product of careful thought about layout, font, and color scheme. Not only does the current web document current design trends, historical snapshots of the web are a lens into past fashions. The Internet Archive cite{internetarchive} has captured snapshots of the public Internet each year going back to 1996. In this paper, we present a curated dataset of 21 years of web design, scraped from the Internet Archive. We report initial analysis of design trends apparent in this data, and we demonstrate how the data can be modeled with deep neural networks to enable novel design applications, such as predicting the apparent year of a web design. The novelty of our work is two-fold: (1) mining the long-term temporal evolution of designs on the Internet, and (2) using deep neural networks as a tool for discovering design elements, which can complement the hand-curated features so far used in data-driven design mining.

Culturally-Grounded Analysis of Everyday Creativity in Social Media: A Case Study in Qatari Context

In deploying social media and other information technologies often not designed with MENA (the Middle East and North Africa) cultures in mind, users generate creative approaches to self-representation using virtual identities while preserving their cultural values. To understand and further empower such approaches, we present a mixed-method of computational and qualitative study, focusing on Qatar as a case of such communities in the MENA region. We analyzed a dataset of over 42,000 publicly available social media profiles using computational approaches (archetypal analysis) and qualitatively analyzed a separate set of 255 profiles. We augmented our descriptions with semi-structured interviews. As a result, we delineate a set of five needs/values exhibited by Qatari users supporting their creativity in effectively using virtual identities: Khaleeji features, self-expression, social connections, social monitoring, and physical and virtual identity contrasts. Finally, we propose an initial set of guidelines to support developers of virtual identity systems in better serving these users while preserving their cultural values and creative agency.

Design Mining Color Semantics

Unlike visual arts where the main goals may be abstract, visual design is conceptualized and created to convey a message and communicate with audiences [16, 17, 45, 64, 66, 78, 80, 114, 115, 167]. This is a key but subtle difference between visual arts and visual design. In fact, it is a critical measure of success in applied arts along with the visual appeal of the design. A design message has to be conveyed at first glance. Studies suggest that designers need to make a good first impression only in some few milliseconds [99, 100], and this impression deals with expressive aesthetics [156], which is a matter of visual appeal by itself.

Design Mining Visual Balance

Psychological studies show that visual balance is an innate concept for humans [12, 20], which influences how we perceive visual aesthetics and cognize harmony [56]. There exists a body of work endeavoring to understand visual balance and its relation with symmetry [52] about vertical [19, 61, 73] and horizontal [19] axes, content of the scene [33], color contrast [35, 57], and styles in abstract and representational artworks [38, 39, 68, 70].

On the Legitimacy of Quantifying Aesthetics

Is it legitimate to quantify aesthetics? Aesthetics is thought to be a matter of subjectiveness; on the other hand, design may not be found in formulas. For instance, Jason K. McDonald states “One reason design formulas and routines are inadequate is because design so often addresses the untried, the unproven, and the unknown” [32]. However, we believe that with advances in computing, the phenomenon of big data, and, more importantly, the fundamental difference between art and visual design, it is possible to quantify aesthetics of visual design.