12,065 research outputs found
General highlight detection in sport videos
Attention is a psychological measurement of human reflection against stimulus. We propose a general framework of highlight detection by comparing attention intensity during the watching of sports videos. Three steps are involved: adaptive selection on salient features, unified attention estimation and highlight identification. Adaptive selection computes feature correlation to decide an optimal set of salient features. Unified estimation combines these features by the technique of multi-resolution autoregressive (MAR) and thus creates a temporal curve of attention intensity. We rank the intensity of attention to discriminate boundaries of highlights. Such a framework alleviates semantic uncertainty around sport highlights and leads to an efficient and effective highlight detection. The advantages are as follows: (1) the capability of using data at coarse temporal resolutions; (2) the robustness against noise caused by modality asynchronism, perception uncertainty and feature mismatch; (3) the employment of Markovian constrains on content presentation, and (4) multi-resolution estimation on attention intensity, which enables the precise allocation of event boundaries
Bibliometric cartography of information retrieval research by using co-word analysis
The aim of this study is to map the intellectual structure of the field of Information Retrieval (IR) during the period of 1987-1997. Co-word analysis was employed to reveal patterns and trends in the IR field by measuring the association strengths of terms representative of relevant publications or other texts produced in IR field. Data were collected from Science Citation Index (SCI) and Social Science Citation Index (SSCI) for the period of 1987-1997. In addition to the keywords added by the SCI and SSCI databases, other important keywords were extracted from titles and abstracts manually. These keywords were further standardized using vocabulary control tools. In order to trace the dynamic changes of the IR field, the whole 11-year period was further separated into two consecutive periods: 1987-1991 and 1992-1997. The results show that the IR field has some established research themes and it also changes rapidly to embrace new themes
Texture-based palmprint retrieval using a layered search scheme for personal identification
2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
Virtual restoration of the Ghent altarpiece using crack detection and inpainting
In this paper, we present a new method for virtual restoration of digitized paintings, with the special focus on the Ghent Altarpiece (1432), one of Belgium's greatest masterpieces. The goal of the work is to remove cracks from the digitized painting thereby approximating how the painting looked like before ageing for nearly 600 years and aiding art historical and palaeographical analysis. For crack detection, we employ a multiscale morphological approach, which can cope with greatly varying thickness of the cracks as well as with their varying intensities (from dark to the light ones). Due to the content of the painting (with extremely many fine details) and complex type of cracks (including inconsistent whitish clouds around them), the available inpainting methods do not provide satisfactory results on many parts of the painting. We show that patch-based methods outperform pixel-based ones, but leaving still much room for improvements in this application. We propose a new method for candidate patch selection, which can be combined with different patch-based inpainting methods to improve their performance in crack removal. The results demonstrate improved performance, with less artefacts and better preserved fine details
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval
In this paper, we address the problem of high performance and computationally
efficient content-based video retrieval in large-scale datasets. Current
methods typically propose either: (i) fine-grained approaches employing
spatio-temporal representations and similarity calculations, achieving high
performance at a high computational cost or (ii) coarse-grained approaches
representing/indexing videos as global vectors, where the spatio-temporal
structure is lost, providing low performance but also having low computational
cost. In this work, we propose a Knowledge Distillation framework, which we
call Distill-and-Select (DnS), that starting from a well-performing
fine-grained Teacher Network learns: a) Student Networks at different retrieval
performance and computational efficiency trade-offs and b) a Selection Network
that at test time rapidly directs samples to the appropriate student to
maintain both high retrieval performance and high computational efficiency. We
train several students with different architectures and arrive at different
trade-offs of performance and efficiency, i.e., speed and storage requirements,
including fine-grained students that store index videos using binary
representations. Importantly, the proposed scheme allows Knowledge Distillation
in large, unlabelled datasets -- this leads to good students. We evaluate DnS
on five public datasets on three different video retrieval tasks and
demonstrate a) that our students achieve state-of-the-art performance in
several cases and b) that our DnS framework provides an excellent trade-off
between retrieval performance, computational speed, and storage space. In
specific configurations, our method achieves similar mAP with the teacher but
is 20 times faster and requires 240 times less storage space. Our collected
dataset and implementation are publicly available:
https://github.com/mever-team/distill-and-select
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