10,013 research outputs found
A survey of comics research in computer science
Graphical novels such as comics and mangas are well known all over the world.
The digital transition started to change the way people are reading comics,
more and more on smartphones and tablets and less and less on paper. In the
recent years, a wide variety of research about comics has been proposed and
might change the way comics are created, distributed and read in future years.
Early work focuses on low level document image analysis: indeed comic books are
complex, they contains text, drawings, balloon, panels, onomatopoeia, etc.
Different fields of computer science covered research about user interaction
and content generation such as multimedia, artificial intelligence,
human-computer interaction, etc. with different sets of values. We propose in
this paper to review the previous research about comics in computer science, to
state what have been done and to give some insights about the main outlooks
Morpes: A Model for Personalized Rendering of Web Content on Mobile Devices
With the tremendous growth in the information communication sector, the
mobile phones have become the prime information communication devices. The
convergence of traditional telephony with the modern web enabled communication
in the mobile devices has made the communication much effective and simpler. As
mobile phones are becoming the crucial source of accessing the contents of the
World Wide Web which was originally designed for personal computers, has opened
up a new challenge of accommodating the web contents in to the smaller mobile
devices. This paper proposes an approach towards building a model for rendering
the web pages in mobile devices. The proposed model is based on a
multi-dimensional web page segment evaluation model. The incorporation of
personalization in the proposed model makes the rendering user-centric. The
proposed model is validated with a prototype implementation.Comment: 10 Pages, 2 Figure
Context and Keyword Extraction in Plain Text Using a Graph Representation
Document indexation is an essential task achieved by archivists or automatic
indexing tools. To retrieve relevant documents to a query, keywords describing
this document have to be carefully chosen. Archivists have to find out the
right topic of a document before starting to extract the keywords. For an
archivist indexing specialized documents, experience plays an important role.
But indexing documents on different topics is much harder. This article
proposes an innovative method for an indexing support system. This system takes
as input an ontology and a plain text document and provides as output
contextualized keywords of the document. The method has been evaluated by
exploiting Wikipedia's category links as a termino-ontological resources
A Model for Personalized Keyword Extraction from Web Pages using Segmentation
The World Wide Web caters to the needs of billions of users in heterogeneous
groups. Each user accessing the World Wide Web might have his / her own
specific interest and would expect the web to respond to the specific
requirements. The process of making the web to react in a customized manner is
achieved through personalization. This paper proposes a novel model for
extracting keywords from a web page with personalization being incorporated
into it. The keyword extraction problem is approached with the help of web page
segmentation which facilitates in making the problem simpler and solving it
effectively. The proposed model is implemented as a prototype and the
experiments conducted on it empirically validate the model's efficiency.Comment: 6 Pages, 2 Figure
Giving order to image queries
Users of image retrieval systems often find it frustrating that the image they are looking for is not ranked near the top of the results they are presented. This paper presents a computational approach for ranking keyworded images in order of relevance to a given keyword. Our approach uses machine learning to attempt to learn what visual features within an image are most related to the keywords, and then provide ranking based on similarity to a visual aggregate. To evaluate the technique, a Web 2.0 application has been developed to obtain a corpus of user-generated ranking information for a given image collection that can be used to evaluate the performance of the ranking algorithm
Indexing, browsing and searching of digital video
Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a âpiece â of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver
Deep Learning for Semantic Part Segmentation with High-Level Guidance
In this work we address the task of segmenting an object into its parts, or
semantic part segmentation. We start by adapting a state-of-the-art semantic
segmentation system to this task, and show that a combination of a
fully-convolutional Deep CNN system coupled with Dense CRF labelling provides
excellent results for a broad range of object categories. Still, this approach
remains agnostic to high-level constraints between object parts. We introduce
such prior information by means of the Restricted Boltzmann Machine, adapted to
our task and train our model in an discriminative fashion, as a hidden CRF,
demonstrating that prior information can yield additional improvements. We also
investigate the performance of our approach ``in the wild'', without
information concerning the objects' bounding boxes, using an object detector to
guide a multi-scale segmentation scheme. We evaluate the performance of our
approach on the Penn-Fudan and LFW datasets for the tasks of pedestrian parsing
and face labelling respectively. We show superior performance with respect to
competitive methods that have been extensively engineered on these benchmarks,
as well as realistic qualitative results on part segmentation, even for
occluded or deformable objects. We also provide quantitative and extensive
qualitative results on three classes from the PASCAL Parts dataset. Finally, we
show that our multi-scale segmentation scheme can boost accuracy, recovering
segmentations for finer parts.Comment: 11 pages (including references), 3 figures, 2 table
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