An Image Is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures

The dynamics of attention in social media tend to obey power laws. Attention concentrates on a relatively small number of popular items and neglecting the vast majority of content produced by the crowd. Although popularity can be an indication of the perceived value of an item within its community, previous research has hinted to the fact that popularity is distinct from intrinsic quality. As a result, content with low visibility but high quality lurks in the tail of the popularity distribution. This phenomenon can be particularly evident in the case of photo-sharing communities, where valuable photographers who are not highly engaged in online social interactions contribute with high-quality pictures that remain unseen. We propose to use a computer vision method to surface beautiful pictures from the immense pool of near-zero-popularity items, and we test it on a large dataset of creative-commons photos on Flickr. By gathering a large crowdsourced ground truth of aesthetics scores for Flickr images, we show that our method retrieves photos whose median perceived beauty score is equal to the most popular ones, and whose average is lower by only 1.5%.Comment: ICWSM 201

Preference Modeling in Data-Driven Product Design: Application in Visual Aesthetics

Creating a form that is attractive to the intended market audience is one of the greatest challenges in product development given the subjective nature of preference and heterogeneous market segments with potentially different product preferences. Accordingly, product designers use a variety of qualitative and quantitative research tools to assess product preferences across market segments, such as design theme clinics, focus groups, customer surveys, and design reviews; however, these tools are still limited due to their dependence on subjective judgment, and being time and resource intensive. In this dissertation, we focus on a key research question: how can we understand and predict more reliably the preference for a future product in heterogeneous markets, so that this understanding can inform designers' decision-making? We present a number of data-driven approaches to model product preference. Instead of depending on any subjective judgment from human, the proposed preference models investigate the mathematical patterns behind users’ choice and behavior. This allows a more objective translation of customers' perception and preference into analytical relations that can inform design decision-making. Moreover, these models are scalable in that they have the capacity to analyze large-scale data and model customer heterogeneity accurately across market segments. In particular, we use feature representation as an intermediate step in our preference model, so that we can not only increase the predictive accuracy of the model but also capture in-depth insight into customers' preference. We tested our data-driven approaches with applications in visual aesthetics preference. Our results show that the proposed approaches can obtain an objective measurement of aesthetic perception and preference for a given market segment. This measurement enables designers to reliably evaluate and predict the aesthetic appeal of their designs. We also quantify the relative importance of aesthetic attributes when both aesthetic attributes and functional attributes are considered by customers. This quantification has great utility in helping product designers and executives in design reviews and selection of designs. Moreover, we visualize the possible factors affecting customers' perception of product aesthetics and how these factors differ across different market segments. Those visualizations are incredibly important to designers as they relate physical design details to psychological customer reactions. The main contribution of this dissertation is to present purely data-driven approaches that enable designers to quantify and interpret more reliably the product preference. Methodological contributions include using modern probabilistic approaches and feature learning algorithms to quantitatively model the design process involving product aesthetics. These novel approaches can not only increase the predictive accuracy but also capture insights to inform design decision-making.PHDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145987/1/yanxinp_1.pd

Attention Allocation Aid for Visual Search

This paper outlines the development and testing of a novel, feedback-enabled attention allocation aid (AAAD), which uses real-time physiological data to improve human performance in a realistic sequential visual search task. Indeed, by optimizing over search duration, the aid improves efficiency, while preserving decision accuracy, as the operator identifies and classifies targets within simulated aerial imagery. Specifically, using experimental eye-tracking data and measurements about target detectability across the human visual field, we develop functional models of detection accuracy as a function of search time, number of eye movements, scan path, and image clutter. These models are then used by the AAAD in conjunction with real time eye position data to make probabilistic estimations of attained search accuracy and to recommend that the observer either move on to the next image or continue exploring the present image. An experimental evaluation in a scenario motivated from human supervisory control in surveillance missions confirms the benefits of the AAAD.Comment: To be presented at the ACM CHI conference in Denver, Colorado in May 201

Data-driven modelling of perceptual properties of 3D shapes

The recent surge in 3D content generation has led to the evolution of difficult to search, organise and re-use massive online 3D visual content libraries. We explore crowdsourcing and machine learning techniques to help alleviate these difficulties by focusing on the visual perceptual properties of 3D shapes. We study “style similarity” and “aesthetics” as two fundamental perceptual properties of 3D shapes and build data-driven models. We rely on crowdsourcing platforms to collect large number of human judgements on style matching and aesthetics of 3D shapes. The judgement data collected directly from humans is used to learn metrics of style matching and aesthetics. Our style similarity measure can be used to compute style distance between a pair of input 3D shapes. In contrast to previous work, we incorporate colour and texture in addition to geometric features to build a colour and texture aware style similarity metric. We also experiment with learning objective and personalised style metrics 3D shapes. The application prototypes we build demonstrate the use of style based search and scene composition. Further, our style distance metric is built iteratively to consume lesser amount of human style judgement data compared to previous methods. We study the problem of building a data-driven model of 3D shape aesthetics in two steps. We first focus on designing a study to crowdsource human aesthetics judgement data. We then formulate a deep learning based strategy to learn a measure of 3D shape aesthetics from collected data. The results of the study in first step helped us choose an appropriate shape representation i.e. voxels as an input to deep neural networks for learning a measure of visual aesthetics. In the same crowdsourcing study, we experiment with the use of polygonal, volumetric, and point based shape representations to create shape stimuli to collect and compare human shape aesthetics judgements. On analysis of the collected data we found that that humans can reliably distinguish more aesthetic shape in a pair even from coarser shape representations such as voxels. This observation implies that detailed shape representations are not needed to compare aesthetics in pairs. The aesthetic value of a 3D shape has traditionally been explored in terms of specific visual features (or handcrafted features) such as curvature and symmetry. For example, more symmetric and curved shapes are considered aesthetic compared to less curved and symmetric shapes. We call such properties as pre-existing notion (or rules) of aesthetics. In order to develop a measure of perceptual aesthetics of 3D shapes which is independent of any pre-existing notion or shape features, we train deep neural networks directly on human aesthetics judgement data. We demonstrate the usefulness of the learned measure by designing applications to rank a collection of shapes based on their aesthetics scores and interactively build scenes using shapes with high aesthetics scores. The overarching goal of this thesis is to demonstrate the use of machine learning and crowdsourcing approaches to build data-driven models of visual perceptual properties of 3D shapes for applications in search, organisation, scene composition, and visualisation of 3D shape data present in ever increasing online 3D shape content libraries. We believe that our exploration of perceptual properties of 3D shapes will motivate further research by looking into other important perceptual properties related to our vision system and will also fuel development of techniques to automatically enhance such properties of a given 3D shape