1,264 research outputs found
Using Apache Lucene to Search Vector of Locally Aggregated Descriptors
Surrogate Text Representation (STR) is a profitable solution to efficient
similarity search on metric space using conventional text search engines, such
as Apache Lucene. This technique is based on comparing the permutations of some
reference objects in place of the original metric distance. However, the
Achilles heel of STR approach is the need to reorder the result set of the
search according to the metric distance. This forces to use a support database
to store the original objects, which requires efficient random I/O on a fast
secondary memory (such as flash-based storages). In this paper, we propose to
extend the Surrogate Text Representation to specifically address a class of
visual metric objects known as Vector of Locally Aggregated Descriptors (VLAD).
This approach is based on representing the individual sub-vectors forming the
VLAD vector with the STR, providing a finer representation of the vector and
enabling us to get rid of the reordering phase. The experiments on a publicly
available dataset show that the extended STR outperforms the baseline STR
achieving satisfactory performance near to the one obtained with the original
VLAD vectors.Comment: In Proceedings of the 11th Joint Conference on Computer Vision,
Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) -
Volume 4: VISAPP, p. 383-39
Lucene4IR: Developing information retrieval evaluation resources using Lucene
The workshop and hackathon on developing Information Retrieval Evaluation Resources using Lucene (L4IR) was held on the 8th and 9th of September, 2016 at the University of Strathclyde in Glasgow, UK and funded by the ESF Elias Network. The event featured three main elements: (i) a series of keynote and invited talks on industry, teaching and evaluation; (ii) planning, coding and hacking where a number of groups created modules and infrastructure to use Lucene to undertake TREC based evaluations; and (iii) a number of breakout groups discussing challenges, opportunities and problems in bridging the divide between academia and industry, and how we can use Lucene for teaching and learning Information Retrieval (IR). The event was composed of a mix and blend of academics, experts and students wanting to learn, share and create evaluation resources for the community. The hacking was intense and the discussions lively creating the basis of many useful tools but also raising numerous issues. It was clear that by adopting and contributing to most widely used and supported Open Source IR toolkit, there were many benefits for academics, students, researchers, developers and practitioners - providing a basis for stronger evaluation practices, increased reproducibility, more efficient knowledge transfer, greater collaboration between academia and industry, and shared teaching and training resources
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
A MultiAgent System for Choosing Software Patterns
Software patterns enable an efficient transfer of design experience by documenting common solutions to recurring design problems. They contain valuable knowledge that can be reused by others, in particular, by less experienced developers. Patterns have been published for system architecture and detailed design, as well as for specific application domains (e.g. agents and security). However, given the steadily growing number of patterns in the literature and online repositories, it can be hard for non-experts to select patterns appropriate to their needs, or even to be aware of the existing patterns. In this paper, we present a multi-agent system that supports developers in choosing patterns that are suitable for a given design problem. The system implements an implicit culture approach for recommending patterns to developers based on the history of decisions made by other developers regarding which patterns to use in related design problems. The recommendations are complemented with the documents from a pattern repository that can be accessed by the agents. The paper includes a set of experimental results obtained using a repository of security patterns. The results prove the viability of the proposed approach
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