1,426 research outputs found
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification
Recent work on scene classification still makes use of generic CNN features
in a rudimentary manner. In this ICCV 2015 paper, we present a novel pipeline
built upon deep CNN features to harvest discriminative visual objects and parts
for scene classification. We first use a region proposal technique to generate
a set of high-quality patches potentially containing objects, and apply a
pre-trained CNN to extract generic deep features from these patches. Then we
perform both unsupervised and weakly supervised learning to screen these
patches and discover discriminative ones representing category-specific objects
and parts. We further apply discriminative clustering enhanced with local CNN
fine-tuning to aggregate similar objects and parts into groups, called meta
objects. A scene image representation is constructed by pooling the feature
response maps of all the learned meta objects at multiple spatial scales. We
have confirmed that the scene image representation obtained using this new
pipeline is capable of delivering state-of-the-art performance on two popular
scene benchmark datasets, MIT Indoor 67~\cite{MITIndoor67} and
Sun397~\cite{Sun397}Comment: To Appear in ICCV 201
Signature Sequence of Intersection Curve of Two Quadrics for Exact Morphological Classification
We present an efficient method for classifying the morphology of the
intersection curve of two quadrics (QSIC) in PR3, 3D real projective space;
here, the term morphology is used in a broad sense to mean the shape,
topological, and algebraic properties of a QSIC, including singularity,
reducibility, the number of connected components, and the degree of each
irreducible component, etc. There are in total 35 different QSIC morphologies
with non-degenerate quadric pencils. For each of these 35 QSIC morphologies,
through a detailed study of the eigenvalue curve and the index function jump we
establish a characterizing algebraic condition expressed in terms of the Segre
characteristics and the signature sequence of a quadric pencil. We show how to
compute a signature sequence with rational arithmetic so as to determine the
morphology of the intersection curve of any two given quadrics. Two immediate
applications of our results are the robust topological classification of QSIC
in computing B-rep surface representation in solid modeling and the derivation
of algebraic conditions for collision detection of quadric primitives
An upper bound for the crossing number of augmented cubes
A {\it good drawing} of a graph is a drawing where the edges are
non-self-intersecting and each two edges have at most one point in common,
which is either a common end vertex or a crossing. The {\it crossing number} of
a graph is the minimum number of pairwise intersections of edges in a good
drawing of in the plane. The {\it -dimensional augmented cube} ,
proposed by S.A. Choudum and V. Sunitha, is an important interconnection
network with good topological properties and applications. In this paper, we
obtain an upper bound on the crossing number of less than
.Comment: 39 page
Speaker-following Video Subtitles
We propose a new method for improving the presentation of subtitles in video
(e.g. TV and movies). With conventional subtitles, the viewer has to constantly
look away from the main viewing area to read the subtitles at the bottom of the
screen, which disrupts the viewing experience and causes unnecessary eyestrain.
Our method places on-screen subtitles next to the respective speakers to allow
the viewer to follow the visual content while simultaneously reading the
subtitles. We use novel identification algorithms to detect the speakers based
on audio and visual information. Then the placement of the subtitles is
determined using global optimization. A comprehensive usability study indicated
that our subtitle placement method outperformed both conventional
fixed-position subtitling and another previous dynamic subtitling method in
terms of enhancing the overall viewing experience and reducing eyestrain
Fast B-spline Curve Fitting by L-BFGS
We propose a novel method for fitting planar B-spline curves to unorganized
data points. In traditional methods, optimization of control points and foot
points are performed in two very time-consuming steps in each iteration: 1)
control points are updated by setting up and solving a linear system of
equations; and 2) foot points are computed by projecting each data point onto a
B-spline curve. Our method uses the L-BFGS optimization method to optimize
control points and foot points simultaneously and therefore it does not need to
perform either matrix computation or foot point projection in every iteration.
As a result, our method is much faster than existing methods
The crossing number of locally twisted cubes
The {\it crossing number} of a graph is the minimum number of pairwise
intersections of edges in a drawing of . Motivated by the recent work
[Faria, L., Figueiredo, C.M.H. de, Sykora, O., Vrt'o, I.: An improved upper
bound on the crossing number of the hypercube. J. Graph Theory {\bf 59},
145--161 (2008)] which solves the upper bound conjecture on the crossing number
of -dimensional hypercube proposed by Erd\H{o}s and Guy, we give upper and
lower bounds of the crossing number of locally twisted cube, which is one of
variants of hypercube.Comment: 17 pages, 12 figure
Robust Component-based Network Localization with Noisy Range Measurements
Accurate and robust localization is crucial for wireless ad-hoc and sensor
networks. Among the localization techniques, component-based methods advance
themselves for conquering network sparseness and anchor sparseness. But
component-based methods are sensitive to ranging noises, which may cause a huge
accumulated error either in component realization or merging process. This
paper presents three results for robust component-based localization under
ranging noises. (1) For a rigid graph component, a novel method is proposed to
evaluate the graph's possible number of flip ambiguities under noises. In
particular, graph's \emph{MInimal sepaRators that are neaRly cOllineaR
(MIRROR)} is presented as the cause of flip ambiguity, and the number of
MIRRORs indicates the possible number of flip ambiguities under noise. (2) Then
the sensitivity of a graph's local deforming regarding ranging noises is
investigated by perturbation analysis. A novel Ranging Sensitivity Matrix (RSM)
is proposed to estimate the node location perturbations due to ranging noises.
(3) By evaluating component robustness via the flipping and the local deforming
risks, a Robust Component Generation and Realization (RCGR) algorithm is
developed, which generates components based on the robustness metrics. RCGR was
evaluated by simulations, which showed much better noise resistance and
locating accuracy improvements than state-of-the-art of component-based
localization algorithms.Comment: 9 pages, 15 figures, ICCCN 2018, Hangzhou, Chin
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