81,439 research outputs found
Sparse Transfer Learning for Interactive Video Search Reranking
Visual reranking is effective to improve the performance of the text-based
video search. However, existing reranking algorithms can only achieve limited
improvement because of the well-known semantic gap between low level visual
features and high level semantic concepts. In this paper, we adopt interactive
video search reranking to bridge the semantic gap by introducing user's
labeling effort. We propose a novel dimension reduction tool, termed sparse
transfer learning (STL), to effectively and efficiently encode user's labeling
information. STL is particularly designed for interactive video search
reranking. Technically, it a) considers the pair-wise discriminative
information to maximally separate labeled query relevant samples from labeled
query irrelevant ones, b) achieves a sparse representation for the subspace to
encodes user's intention by applying the elastic net penalty, and c) propagates
user's labeling information from labeled samples to unlabeled samples by using
the data distribution knowledge. We conducted extensive experiments on the
TRECVID 2005, 2006 and 2007 benchmark datasets and compared STL with popular
dimension reduction algorithms. We report superior performance by using the
proposed STL based interactive video search reranking.Comment: 17 page
Creation of virtual worlds from 3D models retrieved from content aware networks based on sketch and image queries
The recent emergence of user generated content requires new content creation tools that will be both easy to learn and easy to use. These new tools should enable the user to construct new high-quality content with minimum effort; it is essential to allow existing multimedia content to be reused as building blocks when creating new content. In this work we present a new tool for automatically constructing virtual worlds with minimum user intervention. Users can create these worlds by drawing a simple sketch, or by using interactively segmented 2D objects from larger images. The system receives as a query the sketch or the segmented image, and uses it to find similar 3D models that are stored in a Content Centric Network. The user selects a suitable model from the retrieved models, and the system uses it to automatically construct a virtual 3D world
gamma-sky.net: Portal to the Gamma-Ray Sky
Gamma-sky.net is a novel interactive website designed for exploring the
gamma-ray sky. The Map View portion of the site is powered by the Aladin Lite
sky atlas, providing a scalable survey image tesselated onto a
three-dimensional sphere. The map allows for interactive pan and zoom
navigation as well as search queries by sky position or object name. The
default image overlay shows the gamma-ray sky observed by the Fermi-LAT
gamma-ray space telescope. Other survey images (e.g. Planck microwave images in
low/high frequency bands, ROSAT X-ray image) are available for comparison with
the gamma-ray data. Sources from major gamma-ray source catalogs of interest
(Fermi-LAT 2FHL, 3FGL and a TeV source catalog) are overlaid over the sky map
as markers. Clicking on a given source shows basic information in a popup, and
detailed pages for every source are available via the Catalog View component of
the website, including information such as source classification, spectrum and
light-curve plots, and literature references.
We intend for gamma-sky.net to be applicable for both professional
astronomers as well as the general public. The website started in early June
2016 and is being developed as an open-source, open data project on GitHub
(https://github.com/gammapy/gamma-sky). We plan to extend it to display more
gamma-ray and multi-wavelength data. Feedback and contributions are very
welcome!Comment: 6th International Meeting on High Energy Gamma-Ray Astronomy,
Heidelberg, 2016. 6 pages, 5 figures. Website: http://gamma-sky.ne
K-Space at TRECVid 2007
In this paper we describe K-Space participation in
TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance.
The first of the two systems was a ‘shot’ based interface,
where the results from a query were presented as a ranked
list of shots. The second interface was ‘broadcast’ based,
where results were presented as a ranked list of broadcasts.
Both systems made use of the outputs of our high-level feature submission as well as low-level visual features
FaceShop: Deep Sketch-based Face Image Editing
We present a novel system for sketch-based face image editing, enabling users
to edit images intuitively by sketching a few strokes on a region of interest.
Our interface features tools to express a desired image manipulation by
providing both geometry and color constraints as user-drawn strokes. As an
alternative to the direct user input, our proposed system naturally supports a
copy-paste mode, which allows users to edit a given image region by using parts
of another exemplar image without the need of hand-drawn sketching at all. The
proposed interface runs in real-time and facilitates an interactive and
iterative workflow to quickly express the intended edits. Our system is based
on a novel sketch domain and a convolutional neural network trained end-to-end
to automatically learn to render image regions corresponding to the input
strokes. To achieve high quality and semantically consistent results we train
our neural network on two simultaneous tasks, namely image completion and image
translation. To the best of our knowledge, we are the first to combine these
two tasks in a unified framework for interactive image editing. Our results
show that the proposed sketch domain, network architecture, and training
procedure generalize well to real user input and enable high quality synthesis
results without additional post-processing.Comment: 13 pages, 20 figure
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
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