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Graph-Based Representation and Coding of Multiview Images

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PrefEdge: SSD Prefetcher for Large-Scale Graph Traversal

Amitabha Roy

Mining large graphs has now become an important aspect of multiple diverse applications and a number of computer systems have been proposed to provide runtime support. Recent interest in this area has led to the construction of single machine graph computa ...

2014

Graph-Based vs Depth-Based Data Representation for Multiview Images

Pascal Frossard, Thomas Maugey

In this paper, we propose a representation and coding method for multiview images. As an alternative to depth-based schemes, we propose a representation that captures the geometry and the dependencies between pixels in different views in the form of connec ...

2013

Graph-based representation and coding of multiview geometry

Pascal Frossard, Thomas Maugey

We propose a new approach for describing the geometry information of multiview image representations. Rather than transmitting the raw geometry of the scene, under the form of depth information, we build a graph that represents the connections between corr ...

2013

Multiview image coding using graph-based approach

Pascal Frossard, Thomas Maugey

In this paper, we design a new approach for coding the geometry information in a multiview image scenario. As an alternative to depth-based schemes, we propose a representation that captures the dependencies between pixels in different frames in the form o ...

2013

Constrained Binary Identification Problem

Amin Karbasi, Morteza Zadimoghaddam

We consider the problem of building a binary decision tree, to locate an object within a set by way of the least number of membership queries. This problem is equivalent to the “20 questions game” of information theory and is closely related to lossless so ...

2013

Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks

Michael Christoph Gastpar, Naveen Goela

A model called the linear transform network (LTN) is proposed to analyze the compression and estimation of correlated signals transmitted over directed acyclic graphs (DAGs). An LTN is a DAG network with multiple source and receiver nodes. Source nodes tra ...

Institute of Electrical and Electronics Engineers2012

Chebyshev polynomial approximation for transductive learning on graphs

Nathanaël Perraudin

The goal of transductive learning is to find a way to recover the labels of lots of data with only a few known samples. In this work, we will work on graphs for two reasons. First, it’s possible to construct a graph from a given dataset with features. The ...

2011

Space crossing numbers

We define the crossing number for an embedding of a graph G into R^3, and prove a lower bound on it which almost implies the classical crossing lemma. We also give sharp bounds on the space crossing numbers of pseudo-random graphs. ...

2011

Semi-Supervised Learning with Spectral Graph Wavelets

Pierre Vandergheynst, David Shuman, Mohammadjavad Faraji

We consider the transductive learning problem when the labels belong to a continuous space. Through the use of spectral graph wavelets, we explore the benefits of multiresolution analysis on a graph constructed from the labeled and unlabeled data. The spec ...

2011

Complexity of the critical node problem over trees

Marco Di Summa

In this paper we deal with the critical node problem (CNP), i.e., the problem of searching for a given number K of nodes in a graph G, whose removal minimizes the (weighted or unweighted) number of connections between pairs of nodes in the residual graph. ...

2011