6,640 research outputs found
Exploiting multimedia in creating and analysing multimedia Web archives
The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general
Visualizing and Interacting with Concept Hierarchies
Concept Hierarchies and Formal Concept Analysis are theoretically well
grounded and largely experimented methods. They rely on line diagrams called
Galois lattices for visualizing and analysing object-attribute sets. Galois
lattices are visually seducing and conceptually rich for experts. However they
present important drawbacks due to their concept oriented overall structure:
analysing what they show is difficult for non experts, navigation is
cumbersome, interaction is poor, and scalability is a deep bottleneck for
visual interpretation even for experts. In this paper we introduce semantic
probes as a means to overcome many of these problems and extend usability and
application possibilities of traditional FCA visualization methods. Semantic
probes are visual user centred objects which extract and organize reduced
Galois sub-hierarchies. They are simpler, clearer, and they provide a better
navigation support through a rich set of interaction possibilities. Since probe
driven sub-hierarchies are limited to users focus, scalability is under control
and interpretation is facilitated. After some successful experiments, several
applications are being developed with the remaining problem of finding a
compromise between simplicity and conceptual expressivity
A Unified Community Detection, Visualization and Analysis method
Community detection in social graphs has attracted researchers' interest for
a long time. With the widespread of social networks on the Internet it has
recently become an important research domain. Most contributions focus upon the
definition of algorithms for optimizing the so-called modularity function. In
the first place interest was limited to unipartite graph inputs and partitioned
community outputs. Recently bipartite graphs, directed graphs and overlapping
communities have been investigated. Few contributions embrace at the same time
the three types of nodes. In this paper we present a method which unifies
commmunity detection for the three types of nodes and at the same time merges
partitionned and overlapping communities. Moreover results are visualized in
such a way that they can be analyzed and semantically interpreted. For
validation we experiment this method on well known simple benchmarks. It is
then applied to real data in three cases. In two examples of photos sets with
tagged people we reveal social networks. A second type of application is of
particularly interest. After applying our method to Human Brain Tractography
Data provided by a team of neurologists, we produce clusters of white fibers in
accordance with other well known clustering methods. Moreover our approach for
visualizing overlapping clusters allows better understanding of the results by
the neurologist team. These last results open up the possibility of applying
community detection methods in other domains such as data analysis with
original enhanced performances.Comment: Submitted to Advances in Complex System
Collaborative Feature Learning from Social Media
Image feature representation plays an essential role in image recognition and
related tasks. The current state-of-the-art feature learning paradigm is
supervised learning from labeled data. However, this paradigm requires
large-scale category labels, which limits its applicability to domains where
labels are hard to obtain. In this paper, we propose a new data-driven feature
learning paradigm which does not rely on category labels. Instead, we learn
from user behavior data collected on social media. Concretely, we use the image
relationship discovered in the latent space from the user behavior data to
guide the image feature learning. We collect a large-scale image and user
behavior dataset from Behance.net. The dataset consists of 1.9 million images
and over 300 million view records from 1.9 million users. We validate our
feature learning paradigm on this dataset and find that the learned feature
significantly outperforms the state-of-the-art image features in learning
better image similarities. We also show that the learned feature performs
competitively on various recognition benchmarks
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