Word clouds have emerged as a straightforward and visually appealing visualization method for text. They are used in various contexts as a means to provide an overview by distilling text down to those words that appear with highest frequency. Typically, this is done in a static way as pure text summarization. We think, however, that there is a larger potential to this simple yet powerful visualization paradigm in text analytics. In this work, we explore the usefulness of word clouds for general text analysis tasks. We developed a prototypical system called the Word Cloud Explorer that relies entirely on word clouds as a visualization method. It equips them with advanced natural language processing, sophisticated interaction techniques, and context information. We show how this approach can be effectively used to solve text analysis tasks and evaluate it in a qualitative user study.

Word Cloud Explorer: Text Analytics Based on Word Clouds

Next generation environments will change the way people work and live as they will provide new advances in areas ranging from remote work and education, e-commerce, gaming to information-on-demand. In many of these applications intelligent interpretation of multimedia data such as image, video and audio resources is necessary. In this paper we present an effective approach to handling image repositories providing the user with an intuitive interface of visualising and browsing large collections of pictures. Based on the idea of similarity-based organisation of images where images that are visually similar are located close to each other in visualisation space, images are projected onto a sphere with which the user can interact. Rotating the sphere reveals images of different colours while tilting operations focus on brighter or darker images. Large image collections are handled through a hierarchical approach that brings up similar, previously hidden, images when zooming in on an area. Furthermore, the way images are organised can be interactively changed by the user. Our next generation browsing environment has been successfully tested on a large database of several thousand images.

A next generation browsing environment for large image repositories

Word clouds provide a simple and effective means to visually communicate the most frequent words of text documents. However, only few word cloud visualizations support the contrastive analysis of multiple texts. This paper introduces Concentri Cloud, a layered word cloud layout that merges the words from several text documents into a single visualization. The weighted words are arranged in a concentric layout, with those representing the individual documents on the outer circle and the merged ones on inner circles. Interaction techniques allow to analyze the word cloud composition and to provide details on demand. The approach has been implemented and tested on several examples. A qualitative evaluation indicates the general value of Concentri Cloud and reveals benefits and limitations.

Concentri Cloud: Word Cloud Visualization for Multiple Text Documents

In this chapter we provide a comprehensive overview of the emerging field of visualising and browsing image databases. We start with a brief introduction to content-based image retrieval and the traditional query-by-example search paradigm that many retrieval systems employ. We specify the problems associated with this type of interface, such as users not being able to formulate a query due to not having a target image or concept in mind. The idea of browsing systems is then introduced as a means to combat these issues, harnessing the cognitive power of the human mind in order to speed up image retrieval.We detail common methods in which the often high-dimensional feature data extracted from images can be used to visualise image databases in an intuitive way. Systems using dimensionality reduction techniques, such as multi-dimensional scaling, are reviewed along with those that cluster images using either divisive or agglomerative techniques as well as graph-based visualisations. While visualisation of an image collection is useful for providing an overview of the contained images, it forms only part of an image database navigation system. We therefore also present various methods provided by these systems to allow for interactive browsing of these datasets. A further area we explore are user studies of systems and visualisations where we look at the different evaluations undertaken in order to test usability and compare systems, and highlight the key findings from these studies. We conclude the chapter with several recommendations for future work in this area.

Visualisation and Browsing of Image Databases

Digital methods are increasingly applied to store, structure and analyse vast amounts of musical data. In this context, visualization plays a crucial role, as it assists musicologists and non‐expert users in data analysis and in gaining new knowledge. This survey focuses on this unique link between musicology and visualization. We classify 129 related works according to the visualized data types, and we analyse which visualization techniques were applied for certain research inquiries and to fulfill specific tasks. Next to scientific references, we take commercial music software and public websites into account, that contribute novel concepts of visualizing musicological data. We encounter different aspects of uncertainty as major problems when dealing with musicological data and show how occurring inconsistencies are processed and visually communicated. Drawing from our overview in the field, we identify open challenges for research on the interface of musicology and visualization to be tackled in the future.

A Survey on Visualizations for Musical Data

TagClouds is a popular visualization for the collaborative tags. However it has some instinct problems such as linguistic issues, high semantic density and poor understanding of hierarchical structure and semantic relation between tags. In this paper we investigate the ways to support semantic understanding of collaborative tags and propose an improved visualization named TagClusters. Based on the semantic analysis of the collaborative tags in Last.fm, the semantic similar tags are clustered into different groups and the visual distance represents the semantic similarity between tags, and thus the visualization offers a better semantic understanding of collaborative tags. A comparative evaluation is conducted with TagClouds and TagClusters based on the same tags collection. The results indicate that TagClusters has advantages in supporting efficient browsing, searching, impression formation and matching. In the future work, we will explore the possibilities of supporting tag recom-mendation and tag-based Music Retrieval based on TagClusters.

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TagClusters: Semantic Aggregation of Collaborative Tags beyond TagClouds

Current photo browsers for personal photo collections mostly use the folder structure or camera-recorded time stamps as the only ordering principle. Some also allow manually provided meta-data (tags) for organizing and retrieving photos, but currently, only professional tools allow a pre-grouping of photos by image similarity. We believe that similarity is indeed a useful criterion both for image retrieval and casual browsing, and that the tight integration of content analysis techniques in media UIs in general can lead to more powerful UIs. In this paper we present a prototype, in which we have tightly integrated image analysis techniques and user feedback into a zoomable user interface for browsing and sorting digital photos. We have discussed our system with domain experts and received encouragement as well as valuable ideas for future research.

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PhotoSim: Tightly Integrating Image Analysis into a Photo Browsing UI

In music information retrieval (MIR), there are two main research directions, which are based either on a folder hierarchy and metadata, or on the actual acoustic content. We believe that both content-based and hierarchy-based retrieval have their respective strengths for browsing and organizing music collections, and that the integration of content analysis techniques in metadata-based media UIs can lead to more powerful UIs. In this paper we present a prototype, in which audio analysis techniques and user feedback are integrated into an interactive UI for browsing and organizing large music collections. We also provide visual assistance to support non-visual perception of music. We discussed our system with test users and received encouragement as well as valuable suggestions for future re-search.

https://www.medien.ifi.lmu.de/pubdb/publications/pub/chen2009iui/chen2009iui.pdf

Musicsim: integrating audio analysis and user feedback in an interactive music browsing ui

Over the last few years, online multimedia exchange platforms have experienced a rapid growth. They allow users to share their own content and access other’s in turn and hence form very large public collections of User-Generated Content. While research is mostly looking at photo sharing platforms, such as Flickr, much less is known about online music communities. In this paper we present the results of an observational user study followed by a large-scale online survey, which investigated the behavior and the relevant content generated by the users of Last.fm, one of the most popular music communities. Based on the analysis of the results, we present implications for the usage of UserGenerated Content in online music communities. Then we developed a first prototype based on the implications for improving semantic understanding of collaborative tags. We believe our study gives insights for developing information visualization and recommender systems for online music communities. Author Keywords Online music community, User-Generated Content, user behavior, Last.fm.

How Last.fm Illustrates the Musical World: User Behavior and Relevant User-Generated Content

People's activities around their photo collections are often highly dynamic and unstructured, such as casual browsing and searching or loosely structured storytelling. User interfaces to support such an exploratory behavior are a challenging research question. We explore ways to enhance the flexibility in dealing with photo collections and designed a system named PhotoMagnets. It uses a magnet metaphor in addition to more traditional interface elements in order to support a flexible combination of structured and unstructured photo browsing and searching. In an evaluation we received positive feedback especially on the flexibility provided by this approach.

Ya-Xi Chen

Papers

TagClusters: Semantic Aggregation of Collaborative Tags beyond TagClouds

PhotoSim: Tightly Integrating Image Analysis into a Photo Browsing UI

Musicsim: integrating audio analysis and user feedback in an interactive music browsing ui

How Last.fm Illustrates the Musical World: User Behavior and Relevant User-Generated Content

PhotoMagnets: supporting flexible browsing and searching in photo collections