3,978 research outputs found
GraphMatch: Efficient Large-Scale Graph Construction for Structure from Motion
We present GraphMatch, an approximate yet efficient method for building the
matching graph for large-scale structure-from-motion (SfM) pipelines. Unlike
modern SfM pipelines that use vocabulary (Voc.) trees to quickly build the
matching graph and avoid a costly brute-force search of matching image pairs,
GraphMatch does not require an expensive offline pre-processing phase to
construct a Voc. tree. Instead, GraphMatch leverages two priors that can
predict which image pairs are likely to match, thereby making the matching
process for SfM much more efficient. The first is a score computed from the
distance between the Fisher vectors of any two images. The second prior is
based on the graph distance between vertices in the underlying matching graph.
GraphMatch combines these two priors into an iterative "sample-and-propagate"
scheme similar to the PatchMatch algorithm. Its sampling stage uses Fisher
similarity priors to guide the search for matching image pairs, while its
propagation stage explores neighbors of matched pairs to find new ones with a
high image similarity score. Our experiments show that GraphMatch finds the
most image pairs as compared to competing, approximate methods while at the
same time being the most efficient.Comment: Published at IEEE 3DV 201
Cluster-Wise Ratio Tests for Fast Camera Localization
Feature point matching for camera localization suffers from scalability
problems. Even when feature descriptors associated with 3D scene points are
locally unique, as coverage grows, similar or repeated features become
increasingly common. As a result, the standard distance ratio-test used to
identify reliable image feature points is overly restrictive and rejects many
good candidate matches. We propose a simple coarse-to-fine strategy that uses
conservative approximations to robust local ratio-tests that can be computed
efficiently using global approximate k-nearest neighbor search. We treat these
forward matches as votes in camera pose space and use them to prioritize
back-matching within candidate camera pose clusters, exploiting feature
co-visibility captured by clustering the 3D model camera pose graph. This
approach achieves state-of-the-art camera localization results on a variety of
popular benchmarks, outperforming several methods that use more complicated
data structures and that make more restrictive assumptions on camera pose. We
also carry out diagnostic analyses on a difficult test dataset containing
globally repetitive structure that suggest our approach successfully adapts to
the challenges of large-scale image localization
A survey on tree matching and XML retrieval
International audienceWith the increasing number of available XML documents, numerous approaches for retrieval have been proposed in the literature. They usually use the tree representation of documents and queries to process them, whether in an implicit or explicit way. Although retrieving XML documents can be considered as a tree matching problem between the query tree and the document trees, only a few approaches take advantage of the algorithms and methods proposed by the graph theory. In this paper, we aim at studying the theoretical approaches proposed in the literature for tree matching and at seeing how these approaches have been adapted to XML querying and retrieval, from both an exact and an approximate matching perspective. This study will allow us to highlight theoretical aspects of graph theory that have not been yet explored in XML retrieval
Exploiting multimedia in creating and analysing multimedia Web archives
The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general
- âŠ