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Integrating cluster formation and cluster evaluation in interactive visual analysis
Cluster analysis is a popular method for data investigation where data items are structured into groups called clusters. This analysis involves two sequential steps, namely cluster formation and cluster evaluation. In this paper, we propose the tight integration of cluster formation and cluster evaluation in interactive visual analysis in order to overcome the challenges that relate to the black-box nature of clustering algorithms. We present our conceptual framework in the form of an interactive visual environment. In this realization of our framework, we build upon general concepts such as cluster comparison, clustering tendency, cluster stability and cluster coherence. Additionally, we showcase our framework on the cluster analysis of mixed lipid bilayers
TRECVid 2011 Experiments at Dublin City University
This year the iAd-DCU team participated in three of the assigned TRECVid 2011 tasks; Semantic Indexing (SIN), Interactive Known-Item Search (KIS) and Multimedia Event Detection (MED). For the SIN task we presented three full runs using global features, local features and fusion
of global, local features and relationships between concepts respectively. The evaluation results show that local features achieve better performance, with marginal gains found when introducing global features and relationships between concepts. With regard to our KIS submission, similar to our 2010 KIS experiments, we have implemented an iPad interface to a KIS video search tool.
The aim of this year’s experimentation was to evaluate different display methodologies for KIS interaction. For this work, we integrate a clustering element for keyframes, which operates over MPEG-7 features using k-means clustering. In addition, we employ concept detection, not simply for search, but as a means of choosing most representative keyframes for ranked items. For our experiments we compare the baseline non-clustering system to a clustering system on a topic by topic basis. Finally, for the first time this year the iAd group at DCU has been involved in the MED Task. Two techniques are compared, employing low-level features directly and using concepts as intermediate representations. Evaluation results show promising initial results when performing event detection using concepts as intermediate representations
Video browsing interfaces and applications: a review
We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other
Multimedia information technology and the annotation of video
The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning
Visual analytics in FCA-based clustering
Visual analytics is a subdomain of data analysis which combines both human
and machine analytical abilities and is applied mostly in decision-making and
data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was
developed to detect groups of objects with similar properties under similar
conditions. It is used in Social Network Analysis (SNA) and is a basis for
certain types of recommender systems. The problem of triclustering algorithms
is that they do not always produce meaningful clusters. This article describes
a specific triclustering algorithm and a prototype of a visual analytics
platform for working with obtained clusters. This tool is designed as a testing
frameworkis and is intended to help an analyst to grasp the results of
triclustering and recommender algorithms, and to make decisions on
meaningfulness of certain triclusters and recommendations.Comment: 11 pages, 3 figures, 2 algorithms, 3rd International Conference on
Analysis of Images, Social Networks and Texts (AIST'2014). in Supplementary
Proceedings of the 3rd International Conference on Analysis of Images, Social
Networks and Texts (AIST 2014), Vol. 1197, CEUR-WS.org, 201
Exploratory topic modeling with distributional semantics
As we continue to collect and store textual data in a multitude of domains,
we are regularly confronted with material whose largely unknown thematic
structure we want to uncover. With unsupervised, exploratory analysis, no prior
knowledge about the content is required and highly open-ended tasks can be
supported. In the past few years, probabilistic topic modeling has emerged as a
popular approach to this problem. Nevertheless, the representation of the
latent topics as aggregations of semi-coherent terms limits their
interpretability and level of detail.
This paper presents an alternative approach to topic modeling that maps
topics as a network for exploration, based on distributional semantics using
learned word vectors. From the granular level of terms and their semantic
similarity relations global topic structures emerge as clustered regions and
gradients of concepts. Moreover, the paper discusses the visual interactive
representation of the topic map, which plays an important role in supporting
its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent
Data Analysis (IDA 2015
Facet-Based Browsing in Video Retrieval: A Simulation-Based Evaluation
In this paper we introduce a novel interactive video retrieval approach which uses sub-needs of an information need for querying and organising the search process. The underlying assumption of this approach is that the search effectiveness will be enhanced when employed for interactive video retrieval. We explore the performance bounds of a faceted system by using the simulated user evaluation methodology on TRECVID data sets and also on the logs of a prior user experiment with the system. We discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. The facets are simulated by the use of clustering the video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness
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