Dynamic perceptual mapping

Perceptual maps have been used for decades by market researchers to illuminate them about the similarity between brands in terms of a set of attributes, to position consumers relative to brands in terms of their preferences, or to study how demographic and psychometric variables relate to consumer choice. Invariably these maps are two-dimensional and static. As we enter the era of electronic publishing, the possibilities for dynamic graphics are opening up. We demonstrate the usefulness of introducing motion into perceptual maps through four examples. The first example shows how a perceptual map can be viewed in three dimensions, and the second one moves between two analyses of the data that were collected according to different protocols. In a third example we move from the best view of the data at the individual level to one which focuses on between-group differences in aggregated data. A final example considers the case when several demographic variables or market segments are available for each respondent, showing an animation with increasingly detailed demographic comparisons. These examples of dynamic maps use several data sets from marketing and social science research.Animation, brand-attribute maps, correspondence analysis, multidimensional scaling, perceptual map, visualization

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201