60,181 research outputs found
Target Set Selection in Dense Graph Classes
In this paper we study the Target Set Selection problem from a parameterized complexity perspective. Here for a given graph and a threshold for each vertex the task is to find a set of vertices (called a target set) to activate at the beginning which activates the whole graph during the following iterative process. A vertex outside the active set becomes active if the number of so far activated vertices in its neighborhood is at least its threshold.
We give two parameterized algorithms for a special case where each vertex has the threshold set to the half of its neighbors (the so called Majority Target Set Selection problem) for parameterizations by the neighborhood diversity and the twin cover number of the input graph.
We complement these results from the negative side. We give a hardness proof for the Majority Target Set Selection problem when parameterized by (a restriction of) the modular-width - a natural generalization of both previous structural parameters. We show that the Target Set Selection problem parameterized by the neighborhood diversity when there is no restriction on the thresholds is W[1]-hard
Compact Model Representation for 3D Reconstruction
3D reconstruction from 2D images is a central problem in computer vision.
Recent works have been focusing on reconstruction directly from a single image.
It is well known however that only one image cannot provide enough information
for such a reconstruction. A prior knowledge that has been entertained are 3D
CAD models due to its online ubiquity. A fundamental question is how to
compactly represent millions of CAD models while allowing generalization to new
unseen objects with fine-scaled geometry. We introduce an approach to compactly
represent a 3D mesh. Our method first selects a 3D model from a graph structure
by using a novel free-form deformation FFD 3D-2D registration, and then the
selected 3D model is refined to best fit the image silhouette. We perform a
comprehensive quantitative and qualitative analysis that demonstrates
impressive dense and realistic 3D reconstruction from single images.Comment: 9 pages, 6 figure
Multi-Image Semantic Matching by Mining Consistent Features
This work proposes a multi-image matching method to estimate semantic
correspondences across multiple images. In contrast to the previous methods
that optimize all pairwise correspondences, the proposed method identifies and
matches only a sparse set of reliable features in the image collection. In this
way, the proposed method is able to prune nonrepeatable features and also
highly scalable to handle thousands of images. We additionally propose a
low-rank constraint to ensure the geometric consistency of feature
correspondences over the whole image collection. Besides the competitive
performance on multi-graph matching and semantic flow benchmarks, we also
demonstrate the applicability of the proposed method for reconstructing
object-class models and discovering object-class landmarks from images without
using any annotation.Comment: CVPR 201
Network Sampling: From Static to Streaming Graphs
Network sampling is integral to the analysis of social, information, and
biological networks. Since many real-world networks are massive in size,
continuously evolving, and/or distributed in nature, the network structure is
often sampled in order to facilitate study. For these reasons, a more thorough
and complete understanding of network sampling is critical to support the field
of network science. In this paper, we outline a framework for the general
problem of network sampling, by highlighting the different objectives,
population and units of interest, and classes of network sampling methods. In
addition, we propose a spectrum of computational models for network sampling
methods, ranging from the traditionally studied model based on the assumption
of a static domain to a more challenging model that is appropriate for
streaming domains. We design a family of sampling methods based on the concept
of graph induction that generalize across the full spectrum of computational
models (from static to streaming) while efficiently preserving many of the
topological properties of the input graphs. Furthermore, we demonstrate how
traditional static sampling algorithms can be modified for graph streams for
each of the three main classes of sampling methods: node, edge, and
topology-based sampling. Our experimental results indicate that our proposed
family of sampling methods more accurately preserves the underlying properties
of the graph for both static and streaming graphs. Finally, we study the impact
of network sampling algorithms on the parameter estimation and performance
evaluation of relational classification algorithms
ImageSpirit: Verbal Guided Image Parsing
Humans describe images in terms of nouns and adjectives while algorithms
operate on images represented as sets of pixels. Bridging this gap between how
humans would like to access images versus their typical representation is the
goal of image parsing, which involves assigning object and attribute labels to
pixel. In this paper we propose treating nouns as object labels and adjectives
as visual attribute labels. This allows us to formulate the image parsing
problem as one of jointly estimating per-pixel object and attribute labels from
a set of training images. We propose an efficient (interactive time) solution.
Using the extracted labels as handles, our system empowers a user to verbally
refine the results. This enables hands-free parsing of an image into pixel-wise
object/attribute labels that correspond to human semantics. Verbally selecting
objects of interests enables a novel and natural interaction modality that can
possibly be used to interact with new generation devices (e.g. smart phones,
Google Glass, living room devices). We demonstrate our system on a large number
of real-world images with varying complexity. To help understand the tradeoffs
compared to traditional mouse based interactions, results are reported for both
a large scale quantitative evaluation and a user study.Comment: http://mmcheng.net/imagespirit
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