11,493 research outputs found
Spatial information retrieval and geographical ontologies: an overview of the SPIRIT project
A large proportion of the resources available on the world-wide
web refer to information that may be regarded as geographically
located. Thus most activities and enterprises take place in one or
more places on the Earth's surface and there is a wealth of survey
data, images, maps and reports that relate to specific places or
regions. Despite the prevalence of geographical context, existing
web search facilities are poorly adapted to help people find
information that relates to a particular location. When the name of
a place is typed into a typical search engine, web pages that
include that name in their text will be retrieved, but it is likely
that many resources that are also associated with the place may
not be retrieved. Thus resources relating to places that are inside
the specified place may not be found, nor may be places that are
nearby or that are equivalent but referred to by another name.
Specification of geographical context frequently requires the use
of spatial relationships concerning distance or containment for
example, yet such terminology cannot be understood by existing
search engines. Here we provide a brief survey of existing
facilities for geographical information retrieval on the web, before
describing a set of tools and techniques that are being developed
in the project SPIRIT : Spatially-Aware Information Retrieval on
the Internet (funded by European Commission Framework V
Project IST-2001-35047)
Aggregated Deep Local Features for Remote Sensing Image Retrieval
Remote Sensing Image Retrieval remains a challenging topic due to the special
nature of Remote Sensing Imagery. Such images contain various different
semantic objects, which clearly complicates the retrieval task. In this paper,
we present an image retrieval pipeline that uses attentive, local convolutional
features and aggregates them using the Vector of Locally Aggregated Descriptors
(VLAD) to produce a global descriptor. We study various system parameters such
as the multiplicative and additive attention mechanisms and descriptor
dimensionality. We propose a query expansion method that requires no external
inputs. Experiments demonstrate that even without training, the local
convolutional features and global representation outperform other systems.
After system tuning, we can achieve state-of-the-art or competitive results.
Furthermore, we observe that our query expansion method increases overall
system performance by about 3%, using only the top-three retrieved images.
Finally, we show how dimensionality reduction produces compact descriptors with
increased retrieval performance and fast retrieval computation times, e.g. 50%
faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal
contributio
Thesaurus-assisted search term selection and query expansion: a review of user-centred studies
This paper provides a review of the literature related to the application of domain-specific thesauri in the search and retrieval process. Focusing on studies which adopt a user-centred approach, the review presents a survey of the methodologies and results from empirical studies undertaken on the use of thesauri as sources of term selection for query formulation and expansion during the search process. It summaries the ways in which domain-specific thesauri from different disciplines have been used by various types of users and how these tools aid users in the selection of search terms. The review consists of two main sections covering, firstly studies on thesaurus-aided search term selection and secondly those dealing with query expansion using thesauri. Both sections are illustrated with case studies that have adopted a user-centred approach
Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization
This paper tackles the problem of large-scale image-based localization (IBL)
where the spatial location of a query image is determined by finding out the
most similar reference images in a large database. For solving this problem, a
critical task is to learn discriminative image representation that captures
informative information relevant for localization. We propose a novel
representation learning method having higher location-discriminating power. It
provides the following contributions: 1) we represent a place (location) as a
set of exemplar images depicting the same landmarks and aim to maximize
similarities among intra-place images while minimizing similarities among
inter-place images; 2) we model a similarity measure as a probability
distribution on L_2-metric distances between intra-place and inter-place image
representations; 3) we propose a new Stochastic Attraction and Repulsion
Embedding (SARE) loss function minimizing the KL divergence between the learned
and the actual probability distributions; 4) we give theoretical comparisons
between SARE, triplet ranking and contrastive losses. It provides insights into
why SARE is better by analyzing gradients. Our SARE loss is easy to implement
and pluggable to any CNN. Experiments show that our proposed method improves
the localization performance on standard benchmarks by a large margin.
Demonstrating the broad applicability of our method, we obtained the third
place out of 209 teams in the 2018 Google Landmark Retrieval Challenge. Our
code and model are available at https://github.com/Liumouliu/deepIBL.Comment: ICC
Spatio-textual indexing for geographical search on the web
Many web documents refer to specific geographic localities and many
people include geographic context in queries to web search engines. Standard
web search engines treat the geographical terms in the same way as other terms.
This can result in failure to find relevant documents that refer to the place of
interest using alternative related names, such as those of included or nearby
places. This can be overcome by associating text indexing with spatial indexing
methods that exploit geo-tagging procedures to categorise documents with
respect to geographic space. We describe three methods for spatio-textual
indexing based on multiple spatially indexed text indexes, attaching spatial
indexes to the document occurrences of a text index, and merging text index
access results with results of access to a spatial index of documents. These
schemes are compared experimentally with a conventional text index search
engine, using a collection of geo-tagged web documents, and are shown to be
able to compete in speed and storage performance with pure text indexing
The relationship between IR and multimedia databases
Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud
\ud
Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud
\ud
Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud
\ud
First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud
\ud
Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud
\ud
Third, we add the functionality to process the users' relevance feedback.\ud
\ud
We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud
\ud
We conclude with an outline for implementation of miRRor on top of the Monet extensible database system
Automatic tagging and geotagging in video collections and communities
Automatically generated tags and geotags hold great promise
to improve access to video collections and online communi-
ties. We overview three tasks offered in the MediaEval 2010
benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features
- …