126,866 research outputs found
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling
Communication consists of both meta-information as well as content.
Currently, the automated analysis of such data often focuses either on the
network aspects via social network analysis or on the content, utilizing
methods from text-mining. However, the first category of approaches does not
leverage the rich content information, while the latter ignores the
conversation environment and the temporal evolution, as evident in the
meta-information. In contradiction to communication research, which stresses
the importance of a holistic approach, both aspects are rarely applied
simultaneously, and consequently, their combination has not yet received enough
attention in automated analysis systems. In this work, we aim to address this
challenge by discussing the difficulties and design decisions of such a path as
well as contribute CommAID, a blueprint for a holistic strategy to
communication analysis. It features an integrated visual analytics design to
analyze communication networks through dynamics modeling, semantic pattern
retrieval, and a user-adaptable and problem-specific machine learning-based
retrieval system. An interactive multi-level matrix-based visualization
facilitates a focused analysis of both network and content using inline visuals
supporting cross-checks and reducing context switches. We evaluate our approach
in both a case study and through formative evaluation with eight law
enforcement experts using a real-world communication corpus. Results show that
our solution surpasses existing techniques in terms of integration level and
applicability. With this contribution, we aim to pave the path for a more
holistic approach to communication analysis.Comment: 12 pages, 7 figures, Computer Graphics Forum 2021 (pre-peer reviewed
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The application of user log for online business environment using content-based Image retrieval system
Over the past few years, inter-query learning has gained much attention in the research and development of content-based image retrieval (CBIR) systems. This is largely due to the capability of inter-query approach to enable learning from the retrieval patterns of previous query sessions. However, much of the research works in this field have been focusing on analyzing image retrieval patterns stored in the database. This is not suitable for a dynamic environment such as the World Wide Web (WWW) where images are constantly added or removed. A better alternative is to use an image's visual features to capture the knowledge gained from the previous query sessions. Based on the previous work (Chung et al., 2006), the aim of this paper is to propose a framework of inter-query learning for the WWW-CBIR systems. Such framework can be extremely useful for those online companies whose core business involves providing multimedia content-based services and products to their customers
Language-based multimedia information retrieval
This paper describes various methods and approaches for language-based multimedia information retrieval, which have been developed in the projects POP-EYE and OLIVE and which will be developed further in the MUMIS project. All of these project aim at supporting automated indexing of video material by use of human language technologies. Thus, in contrast to image or sound-based retrieval methods, where both the query language and the indexing methods build on non-linguistic data, these methods attempt to exploit advanced text retrieval technologies for the retrieval of non-textual material. While POP-EYE was building on subtitles or captions as the prime language key for disclosing video fragments, OLIVE is making use of speech recognition to automatically derive transcriptions of the sound tracks, generating time-coded linguistic elements which then serve as the basis for text-based retrieval functionality
Metadata Augmentation for Semantic- and Context- Based Retrieval of Digital Cultural Objects
Cultural objects are increasingly stored and generated in digital form, yet effective methods for their indexing and retrieval still remain an open area of research. The main problem arises from the disconnection between the content-based indexing approach used by computer scientists and the description-based approach used by information scientists. There is also a lack of representational schemes that allow the alignment of the semantics and context with keywords and low-level features that can be automatically extracted from the content of these cultural objects. This paper presents an integrated approach to address these problems, taking advantage of both computer science and information science approaches. The focus is on the rationale and conceptual design of the system and its various components. In particular, we discuss techniques for augmenting commonly used metadata with visual features and domain knowledge to generate high-level abstract metadata which in turn can be used for semantic and context-based indexing and retrieval. We use a sample collection of Vietnamese traditional woodcuts to demonstrate the usefulness of this approach
Multimedia search without visual analysis: the value of linguistic and contextual information
This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features
Object Level Deep Feature Pooling for Compact Image Representation
Convolutional Neural Network (CNN) features have been successfully employed
in recent works as an image descriptor for various vision tasks. But the
inability of the deep CNN features to exhibit invariance to geometric
transformations and object compositions poses a great challenge for image
search. In this work, we demonstrate the effectiveness of the objectness prior
over the deep CNN features of image regions for obtaining an invariant image
representation. The proposed approach represents the image as a vector of
pooled CNN features describing the underlying objects. This representation
provides robustness to spatial layout of the objects in the scene and achieves
invariance to general geometric transformations, such as translation, rotation
and scaling. The proposed approach also leads to a compact representation of
the scene, making each image occupy a smaller memory footprint. Experiments
show that the proposed representation achieves state of the art retrieval
results on a set of challenging benchmark image datasets, while maintaining a
compact representation.Comment: Deep Vision 201
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
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