A Crowdsourcing Procedure for the Discovery of Non-Obvious Attributes of Social Image

Research on mid-level image representations has conventionally concentrated relatively obvious attributes and overlooked non-obvious attributes, i.e., characteristics that are not readily observable when images are viewed independently of their context or function. Non-obvious attributes are not necessarily easily nameable, but nonetheless they play a systematic role in people`s interpretation of images. Clusters of related non-obvious attributes, called interpretation dimensions, emerge when people are asked to compare images, and provide important insight on aspects of social images that are considered relevant. In contrast to aesthetic or affective approaches to image analysis, non-obvious attributes are not related to the personal perspective of the viewer. Instead, they encode a conventional understanding of the world, which is tacit, rather than explicitly expressed. This paper introduces a procedure for discovering non-obvious attributes using crowdsourcing. We discuss this procedure using a concrete example of a crowdsourcing task on Amazon Mechanical Turk carried out in the domain of fashion. An analysis comparing discovered non-obvious attributes with user tags demonstrated the added value delivered by our procedure.Comment: 6 pages, 3 figures, Extended version of paper to appear in CrowdMM 2014: International ACM Workshop on Crowdsourcing for Multimedi

Characterizing Novelty as a Motivator in Online Citizen Science

Citizen science projects rely on the voluntary contribution of nonscientists to take part in scientific research projects. Projects taking place exclusively over the Internet face significant challenges, chief among them is the attracting and keeping the critical mass of volunteers needed to conduct the work outlined by the science team. The extent to which platforms can design experiences that positively influence volunteers’ motivation can help address the contribution challenges. Consequently, project organizers need to develop strategies to attract new participants and keep existing ones. One strategy to encourage participation is implementing features, which re-enforce motives known to change people’s attitudes towards contributing positively. The literature in psychology noted that novelty is an attribute of objects and environments that occasion curiosity in humans leading to exploratory behaviors, e.g., prolonged engagement with the object or environment. This dissertation described the design, implementation, and evaluation of an experiment conducted in three online citizen science projects. Volunteers received novelty cues when they classified data objects that no other volunteer had previously seen. The hypothesis was that exposure to novelty cues while classifying data positively influences motivational attitudes leading to increased engagement in the classification task and increased retention. The experiments resulted in mixed results. In some projects, novelty cues were universally salient, and in other projects, novelty cues had no significant impact on volunteers’ contribution behaviors. The results, while mixed, are promising since differences in the observed behaviors arise because of individual personality differences and the unique attributes found in each project setting. This research contributes to empirically grounded studies on motivation in citizen science with analyses that produce new insights and questions into the functioning of novelty and its impact on volunteers’ behaviors

Towards Commentary-Driven Soccer Player Analytics

Open information extraction (open IE) has been shown to be useful in a number of NLP Tasks, such as question answering, relation extraction, and information retrieval. Soccer is the most watched sport in the world. The dynamic nature of the game corresponds to the team strategy and individual contribution, which are the deciding factors for a team’s success. Generally, companies collect sports event data manually and very rarely they allow free-access to these data by third parties. However, a large amount of data is available freely on various social media platforms where different types of users discuss these very events. To rely on expert data, we are currently using the live-match commentary as our rich and unexplored data-source. Our aim out of this commentary analysis is to initially extract key events from each game and eventually key entities like players involved, player action and other player related attributes from these key events. We propose an end-to-end application to extract commentaries and extract player attributes from it. The study will primarily depend on an extensive crowd labelling of data involving precautionary periodical checks to prevent incorrectly tagged data. This research will contribute significantly towards analysis of commentary and acts as a cheap tool providing player performance analysis for smaller to intermediate budget soccer club

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

Improving digital image retrieval towards image understanding and organization

Ph.DDOCTOR OF PHILOSOPH