55,137 research outputs found
Comparison of full-text versus metadata searching in an institutional repository: Case study of the UNT Scholarly Works
Authors in the library science field disagree about the importance of using
costly resources to create local metadata records, particularly for scholarly
materials that have full-text search alternatives. At the University of North
Texas (UNT) Libraries, we decided to test this concept by answering the
question: What percentage of search terms retrieved results based on full-text
versus metadata values for items in the UNT Scholarly Works institutional
repository? The analysis matched search query logs to indexes of the metadata
records and full text of the items in the collection. Results show the
distribution of item discoveries that were based on metadata exclusively, on
full text exclusively, and on the combination of both. This paper describes in
detail the methods and findings of this study
Dublin City University video track experiments for TREC 2003
In this paper, we describe our experiments for both the News Story Segmentation task and Interactive Search task for
TRECVID 2003. Our News Story Segmentation task involved the use of a Support Vector Machine (SVM) to combine evidence from audio-visual analysis tools in order to generate a listing of news stories from a given news programme. Our
Search task experiment compared a video retrieval system based on text, image and relevance feedback with a text-only
video retrieval system in order to identify which was more effective. In order to do so we developed two variations of our Físchlár video retrieval system and conducted user testing in a controlled lab environment. In this paper we outline our work on both of these two tasks
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Entity Query Feature Expansion Using Knowledge Base Links
Recent advances in automatic entity linking and knowledge base
construction have resulted in entity annotations for document and
query collections. For example, annotations of entities from large
general purpose knowledge bases, such as Freebase and the Google
Knowledge Graph. Understanding how to leverage these entity
annotations of text to improve ad hoc document retrieval is an open
research area. Query expansion is a commonly used technique to
improve retrieval effectiveness. Most previous query expansion
approaches focus on text, mainly using unigram concepts. In this
paper, we propose a new technique, called entity query feature
expansion (EQFE) which enriches the query with features from
entities and their links to knowledge bases, including structured
attributes and text. We experiment using both explicit query entity
annotations and latent entities. We evaluate our technique on TREC
text collections automatically annotated with knowledge base entity
links, including the Google Freebase Annotations (FACC1) data.
We find that entity-based feature expansion results in significant
improvements in retrieval effectiveness over state-of-the-art text
expansion approaches
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Visual localization enables autonomous vehicles to navigate in their
surroundings and augmented reality applications to link virtual to real worlds.
Practical visual localization approaches need to be robust to a wide variety of
viewing condition, including day-night changes, as well as weather and seasonal
variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera
pose estimates. In this paper, we introduce the first benchmark datasets
specifically designed for analyzing the impact of such factors on visual
localization. Using carefully created ground truth poses for query images taken
under a wide variety of conditions, we evaluate the impact of various factors
on 6DOF camera pose estimation accuracy through extensive experiments with
state-of-the-art localization approaches. Based on our results, we draw
conclusions about the difficulty of different conditions, showing that
long-term localization is far from solved, and propose promising avenues for
future work, including sequence-based localization approaches and the need for
better local features. Our benchmark is available at visuallocalization.net.Comment: Accepted to CVPR 2018 as a spotligh
Target Apps Selection: Towards a Unified Search Framework for Mobile Devices
With the recent growth of conversational systems and intelligent assistants
such as Apple Siri and Google Assistant, mobile devices are becoming even more
pervasive in our lives. As a consequence, users are getting engaged with the
mobile apps and frequently search for an information need in their apps.
However, users cannot search within their apps through their intelligent
assistants. This requires a unified mobile search framework that identifies the
target app(s) for the user's query, submits the query to the app(s), and
presents the results to the user. In this paper, we take the first step forward
towards developing unified mobile search. In more detail, we introduce and
study the task of target apps selection, which has various potential real-world
applications. To this aim, we analyze attributes of search queries as well as
user behaviors, while searching with different mobile apps. The analyses are
done based on thousands of queries that we collected through crowdsourcing. We
finally study the performance of state-of-the-art retrieval models for this
task and propose two simple yet effective neural models that significantly
outperform the baselines. Our neural approaches are based on learning
high-dimensional representations for mobile apps. Our analyses and experiments
suggest specific future directions in this research area.Comment: To appear at SIGIR 201
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