We present SurfaceLink, a system where users can make natural surface gestures to control association and information transfer among a set of devices that are placed on a mutually shared surface (e.g., a table). SurfaceLink uses a combination of on-device accelerometers, vibration motors, speakers and microphones (and, optionally, an off-device contact microphone for greater sensitivity) to sense gestures performed on the shared surface. In a controlled evaluation with 10 participants, SurfaceLink detected the presence of devices on the same surface with 97.7% accuracy, their relative arrangement with 89.4% accuracy, and various single- and multi-touch surface gestures with an average accuracy of 90.3%. A usability analysis showed that SurfaceLink has advantages over current multi-device interaction techniques in a number of situations.

As consumers accumulate more and more personal imagery, searching for specific images has become increasingly difficult. Consumers typically provide little or no annotations, and automated classifiers and concept tagging tools are limited in their scope and vocabulary. This work addresses this sparsity of semantic information by leveraging domain-specific information provided by online photo-sharing communities. Such information enables improved search by allowing user-provided search terms to be expanded into a set of semantically related concepts, using relevant semantic relationships provided by millions of users. Our system first extracts metadata using a modest number of image and event-based semantic classifiers, as well as any meaningful file or folder names. When users pose text-based queries, our system retrieves images from their personal image collections by leveraging Flickr's tag dataset for concept expansion. This approach enables users to search their collections without having to manually annotate their pictures. We compare the retrieval performance of using a Flickr-based concept expander with the performance obtained without concept expansion and with using a WordNet-based concept expander. The results demonstrate that common sense knowledge gleaned from online photo sharing communities can enable meaningful image search on consumer image collections, searches that would be impossible using only the available image metadata.

Benchmark and ground truth databases are critical for evaluating imaging- and event-based algorithms, but such databases can be time consuming to create, cannot be representative of all real-life consumer image collections, and do not necessarily provide feedback on the general utility of an imperfect algorithm applied to real-life data. An alternative solution is needed to complement evaluation by benchmark and ground truth databases. We designed and developed event analyzer as a tool to meet this need. Event analyzer combines data visualization of metadata with event and image retrieval results in a way that enables researchers to easily review data trends, quickly filter and select data, see relationships between metadata values, and visually scan retrieved results to determine correctness. In this paper, we describe the tool and discuss how it can and has been used to analyze real-life consumer collections in an event-centric manner.

The "Reminiscing View" is a visualization system for browsing hierarchically organized photo and video-clip collections. The current Reminiscing View prototype supports browsing of hierarchical temporal event clusters. Users simply upload their pictures and video clips to a server and their collection is automatically clustered into hierarchical temporal events. They can then browse events in a chronologically ordered spiral or grid view and explore details of each event. They can also filter the displayed media by date, text-based search, and sharing group. The addition of a remote control interface to the system allows users to browse their collection in a ldquolean backrdquo setting such as a living room environment. This paper describes the system and interface of the Reminiscing View. It also presents results and implications of a user study conducted on an earlier version of the system.

Unorganized media collections hinder consumers from fully experiencing and enjoying their visual media. User interfaces can mediate the results of automated indexing by presenting data and interactions that leverage the strengths of individual and combined algorithm results, supporting multifaceted browsing, and enabling user correction in a way that is not disruptive to the userspsila activities. We describe the semantic system demonstration framework (SSDF), a flexible and extensible framework for combining multiple semantic indexing algorithms for consumer photo and video clip collections into one integrated system. We also describe key features of Koi, an SSDF desktop client application with a user interface designed to mediate and leverage the intelligent indexing incorporated in the SSDF server. Together, Koi and SSDF empower users to experience their personal multimedia in novel and sophisticated ways.

We describe a speech-based interface to an information visualization (infoVis) system. Users ask natural-language questions about a given data domain. Our interface then maps the questions into infoVis operations, which result in the display of data visualizations that address the questions. Users can interact with these views via speech or direct manipulation. If users give incomplete information, our interface guides them in clarifying their questions. The intelligence behind our interface is encapsulated in a service logic that embodies domain knowledge about both the data being explored and the infoVis system. This allows users to focus on answering questions, rather than on the mechanics of accessing data and creating views.

This paper proposes a framework for easily integrating and controlling information visualization (infoVis) components within web pages to create powerful interactive “live” documents, or LiveDocs. The framework includes a set of infoVis components which can be placed and linked within a standard HTML document, initialized to focus on key analysis results, and directly manipulated by readers to explore and analyze data further. In addition, authors can script the manipulation of views at the user interaction level (e.g., to set view options, select items within a view, or animate a view). We illustrate our approach with a sample analysis of a real-life data set.

Our MultiMedia Visual Information Seeking (MMVIS) environment provides an exploratory visual paradigm for temporal trend analysis. In this paper, we present the results of a user interface study evaluating the utility of MMVIS. We compare MMVIS to a timeline-based approach for analyzing temporal trends in real video data. We evaluate the quantity, complexity and accuracy of temporal trend observations made within each interface, compare the number of positive versus negative trends found, and collect feedback on user satisfaction. Our results show that subjects made interesting and complex observations of temporal trends using either interface. The results also indicate some advantages and biases of each interface, such as 1) timeline subjects make more errors during analysis and 2) timeline subjects are biased against identifying negative trends such as exceptions. At the same time, however, subjects appreciate the familiarity of timelines. Because we designed the MMVIS architecture to provide users with a library of visualizations, we thus include a discussion on enhancing the utility of MMVIS through incorporating a timeline into it in the future.

We have developed a MultiMedia Visual Information Seeking (MMVIS) Environment designed to support an integrated approach to direct manipulation temporal querying and browsing of temporal relationship results. We address the optimization of queries specified via our visual query interface. Queries in MMVIS are incrementally specified and continuously refined multidimensional range queries. We present our k-Array index structure and its bucket based counterpart, the k-Bucket, as new indexes optimized for processing these direct manipulation queries. In an experimental evaluation comparing our k-Array and k-Bucket solutions to alternate techniques from the literature, we show that the k-Bucket performs generally equal to or better than the other techniques and is the best overall approach for such environments.

As new query interfaces emerge for accessing multimedia data, formal user studies are needed to evaluate the usability of such interfaces.In this paper, we present results from a user interface evaluation of our temporal visual query language (TVQL).TVQL is a novel direct manipulation query interface for specifying temporal relationship queries over temporal events such as video data.In our user study, we compare TVQL to a forms-based temporal query language (TForms).Our results indicate that while subjects took longer to learn TVQL than TForrns, they were more efficient and more accurate in specifying temporal queries with the TVQL interface than with the TForms interface.

Article Free Access Share on MMVIS: design and implementation of a multimedia visual information seeking environment Authors: Stacie Hibino EECS Dept., Software Systems Research Lab, The University of Michigan, 1301 Beal Avenue, Ann Arbor, MI EECS Dept., Software Systems Research Lab, The University of Michigan, 1301 Beal Avenue, Ann Arbor, MIView Profile , Elke A. Rundensteiner Computer Science Department, Worcester Polytechnic Institute, 100 Institute Rd., Worcester, MA and University of Michigan Computer Science Department, Worcester Polytechnic Institute, 100 Institute Rd., Worcester, MA and University of MichiganView Profile Authors Info & Claims MULTIMEDIA '96: Proceedings of the fourth ACM international conference on MultimediaFebruary 1997 Pages 75–86https://doi.org/10.1145/244130.244149Online:01 February 1997Publication History 12citation467DownloadsMetricsTotal Citations12Total Downloads467Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF