12,627 research outputs found
Accurator: Nichesourcing for Cultural Heritage
With more and more cultural heritage data being published online, their
usefulness in this open context depends on the quality and diversity of
descriptive metadata for collection objects. In many cases, existing metadata
is not adequate for a variety of retrieval and research tasks and more specific
annotations are necessary. However, eliciting such annotations is a challenge
since it often requires domain-specific knowledge. Where crowdsourcing can be
successfully used for eliciting simple annotations, identifying people with the
required expertise might prove troublesome for tasks requiring more complex or
domain-specific knowledge. Nichesourcing addresses this problem, by tapping
into the expert knowledge available in niche communities. This paper presents
Accurator, a methodology for conducting nichesourcing campaigns for cultural
heritage institutions, by addressing communities, organizing events and
tailoring a web-based annotation tool to a domain of choice. The contribution
of this paper is threefold: 1) a nichesourcing methodology, 2) an annotation
tool for experts and 3) validation of the methodology and tool in three case
studies. The three domains of the case studies are birds on art, bible prints
and fashion images. We compare the quality and quantity of obtained annotations
in the three case studies, showing that the nichesourcing methodology in
combination with the image annotation tool can be used to collect high quality
annotations in a variety of domains and annotation tasks. A user evaluation
indicates the tool is suited and usable for domain specific annotation tasks
Optical tomography: Image improvement using mixed projection of parallel and fan beam modes
Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam
Vision systems with the human in the loop
The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed
HCU400: An Annotated Dataset for Exploring Aural Phenomenology Through Causal Uncertainty
The way we perceive a sound depends on many aspects-- its ecological
frequency, acoustic features, typicality, and most notably, its identified
source. In this paper, we present the HCU400: a dataset of 402 sounds ranging
from easily identifiable everyday sounds to intentionally obscured artificial
ones. It aims to lower the barrier for the study of aural phenomenology as the
largest available audio dataset to include an analysis of causal attribution.
Each sample has been annotated with crowd-sourced descriptions, as well as
familiarity, imageability, arousal, and valence ratings. We extend existing
calculations of causal uncertainty, automating and generalizing them with word
embeddings. Upon analysis we find that individuals will provide less polarized
emotion ratings as a sound's source becomes increasingly ambiguous; individual
ratings of familiarity and imageability, on the other hand, diverge as
uncertainty increases despite a clear negative trend on average
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Towards memory supporting personal information management tools
In this article we discuss re-retrieving personal information objects and relate the task to recovering from lapse(s) in memory. We propose that fundamentally it is lapses in memory that impede users from successfully re-finding the information they need. Our hypothesis is that by learning more about memory lapses in non-computing contexts and how people cope and recover from these lapses, we can better inform the design of PIM tools and improve the user's ability to re-access and re-use objects. We describe a diary study that investigates the everyday memory problems of 25 people from a wide range of backgrounds. Based on the findings, we present a series of principles that we hypothesize will improve the design of personal information management tools. This hypothesis is validated by an evaluation of a tool for managing personal photographs, which was designed with respect to our findings. The evaluation suggests that users' performance when re-finding objects can be improved by building personal information management tools to support characteristics of human memory
Semantic bottleneck for computer vision tasks
This paper introduces a novel method for the representation of images that is
semantic by nature, addressing the question of computation intelligibility in
computer vision tasks. More specifically, our proposition is to introduce what
we call a semantic bottleneck in the processing pipeline, which is a crossing
point in which the representation of the image is entirely expressed with
natural language , while retaining the efficiency of numerical representations.
We show that our approach is able to generate semantic representations that
give state-of-the-art results on semantic content-based image retrieval and
also perform very well on image classification tasks. Intelligibility is
evaluated through user centered experiments for failure detection
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