Selecting an optimal set of icons is a crucial step in the pipeline of visual design to structure and navigate through content. However, designing the icons sets is usually a difficult task for which expert knowledge is required. In this work, to ease the process of icon set selection to the users, we propose a similarity metric which captures the properties of style and visual identity. We train a Siamese Neural Network with an on-line dataset of icons organized in visually coherent collections that are used to adaptively sample training data and optimize the training process. As the dataset contains noise, we further collect human-rated information on the perception of icon’s similarity which will be used for evaluating and testing the proposed model. We present several results and applications based on searches, kernel visualizations and optimized set proposals that can be helpful for designers and non-expert users while exploring large collections of icons.
Authors use icon encodings to indicate the semantics of categorical information in visualizations. The default icon libraries found in visualization tools often do not match the semantics of the data. Users often manually search for or create icons that are more semantically meaningful. This process can hinder the flow of visual analysis, especially when the amount of data is large, leading to a suboptimal user experience. We propose a technique for automatically generating semantically relevant icon encodings for categorical dimensions of data points. The algorithm employs natural language processing in order to find relevant imagery from the Internet. We evaluate our approach on Mechanical Turk by generating large libraries of icons using Tableau Public workbooks that represent real analytical effort by people out in the world. Our results show that the automatic algorithm does nearly as well as the manually created icons, and particularly has higher user satisfaction for larger cardinalities of data. Semanticons can enhance the representation of files by offering symbols that are both meaningful and easily distinguishable. The semantics of a file is estimated by parsing its name, location, and content to generate a ‘context’, which is used to query an image database. The resulting images are simplified by segmenting them, computing an importance value for each segmented region, and removing unimportant regions. The Although existing GUIs have a sense of space, they provide no sense of place. Numerous studies report that users misplace files and have trouble wayfinding in virtual worlds despite the fact that people have remarkable visual and spatial abilities. This issue is considered in the human-computer interface field and has been addressed with alternate display navigation schemes. Our paper presents a fundamentally graphics based approach to this 'lost in hyperspace' problem. Specifically, we propose that spatial display of files is not sufficient to engage our visual skills; scenery (distinctive visual appearance) is needed as well. While scenery (in the form of custom icon assignments) is already possible in current operating systems, few if any users take the time to manually assign icons to all their files. As such, our proposal is to generate visually distinctive icons ("VisualIDs") automatically, while allowing the user to replace the icon if desired. The paper discusses psychological and conceptual issues relating to icons, visual memory, and the necessary relation of scenery to data. A particular icon generation algorithm is described; subjects using these icons in simulated file search and recall tasks show significantly improved performance with little effort. Although the incorporation of scenery in a graphical user interface will introduce many new (and interesting) design problems that cannot be addressed in this paper, we show that automatically created scenery is both beneficial and feasible.