6,700 research outputs found
Object Discovery via Cohesion Measurement
Color and intensity are two important components in an image. Usually, groups
of image pixels, which are similar in color or intensity, are an informative
representation for an object. They are therefore particularly suitable for
computer vision tasks, such as saliency detection and object proposal
generation. However, image pixels, which share a similar real-world color, may
be quite different since colors are often distorted by intensity. In this
paper, we reinvestigate the affinity matrices originally used in image
segmentation methods based on spectral clustering. A new affinity matrix, which
is robust to color distortions, is formulated for object discovery. Moreover, a
Cohesion Measurement (CM) for object regions is also derived based on the
formulated affinity matrix. Based on the new Cohesion Measurement, a novel
object discovery method is proposed to discover objects latent in an image by
utilizing the eigenvectors of the affinity matrix. Then we apply the proposed
method to both saliency detection and object proposal generation. Experimental
results on several evaluation benchmarks demonstrate that the proposed CM based
method has achieved promising performance for these two tasks.Comment: 14 pages, 14 figure
A Topic Recommender for Journalists
The way in which people acquire information on events and form their own
opinion on them has changed dramatically with the advent of social media. For many
readers, the news gathered from online sources become an opportunity to share points
of view and information within micro-blogging platforms such as Twitter, mainly
aimed at satisfying their communication needs. Furthermore, the need to deepen the
aspects related to news stimulates a demand for additional information which is often
met through online encyclopedias, such as Wikipedia. This behaviour has also
influenced the way in which journalists write their articles, requiring a careful assessment
of what actually interests the readers. The goal of this paper is to present
a recommender system, What to Write and Why, capable of suggesting to a journalist,
for a given event, the aspects still uncovered in news articles on which the
readers focus their interest. The basic idea is to characterize an event according to
the echo it receives in online news sources and associate it with the corresponding
readers’ communicative and informative patterns, detected through the analysis of
Twitter and Wikipedia, respectively. Our methodology temporally aligns the results
of this analysis and recommends the concepts that emerge as topics of interest from
Twitter and Wikipedia, either not covered or poorly covered in the published news
articles
An Iterative Co-Saliency Framework for RGBD Images
As a newly emerging and significant topic in computer vision community,
co-saliency detection aims at discovering the common salient objects in
multiple related images. The existing methods often generate the co-saliency
map through a direct forward pipeline which is based on the designed cues or
initialization, but lack the refinement-cycle scheme. Moreover, they mainly
focus on RGB image and ignore the depth information for RGBD images. In this
paper, we propose an iterative RGBD co-saliency framework, which utilizes the
existing single saliency maps as the initialization, and generates the final
RGBD cosaliency map by using a refinement-cycle model. Three schemes are
employed in the proposed RGBD co-saliency framework, which include the addition
scheme, deletion scheme, and iteration scheme. The addition scheme is used to
highlight the salient regions based on intra-image depth propagation and
saliency propagation, while the deletion scheme filters the saliency regions
and removes the non-common salient regions based on interimage constraint. The
iteration scheme is proposed to obtain more homogeneous and consistent
co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is
proposed in the addition scheme to introduce the depth information to enhance
identification of co-salient objects. The proposed method can effectively
exploit any existing 2D saliency model to work well in RGBD co-saliency
scenarios. The experiments on two RGBD cosaliency datasets demonstrate the
effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics
2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm
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