9,724 research outputs found
Automatic summarization of rushes video using bipartite graphs
In this paper we present a new approach for automatic summarization of rushes, or unstructured video. Our approach is composed of three major steps. First, based on shot and sub-shot segmentations, we filter sub-shots with low information content not likely to be useful in a summary. Second, a method using maximal matching in a bipartite graph is adapted to measure similarity between the remaining shots and to minimize inter-shot redundancy by removing repetitive retake shots common in rushes video. Finally, the presence of faces and motion intensity are characterised in each sub-shot. A measure of how representative the sub-shot is in the context of the overall video is then proposed. Video summaries composed of keyframe slideshows are then generated. In order to evaluate the effectiveness of this approach we re-run the evaluation carried out by TRECVid, using the same dataset and evaluation metrics used in the TRECVid video summarization task in 2007 but with our own assessors. Results show that our approach leads to a significant improvement on our own work in terms of the fraction of the TRECVid summary ground truth included and is competitive with the best of other approaches in TRECVid 2007
Automatic summarization of rushes video using bipartite graphs
In this paper we present a new approach for automatic summarization of rushes video. Our approach is composed of three main steps. First, based on a temporal segmentation, we filter sub-shots with low information content not likely to be useful in a summary. Second, a method using maximal matching in a bipartite graph is adapted to measure similarity between the remaining shots and to minimize inter-shot redundancy by removing repetitive retake shots common in rushes content. Finally, the presence of faces and the motion intensity are characterised in each sub-shot. A measure of how representative the sub-shot is in the context of the overall video is then proposed. Video summaries composed of keyframe slideshows are then generated. In order to evaluate the effectiveness of this approach we re-run the evaluation carried out by the TREC, using the same dataset and evaluation metrics used in the TRECVID video summarization task in 2007 but with our own assessors. Results show that our approach leads to a significant improvement in terms of the fraction of the TRECVID summary ground truth included and is competitive with other approaches in TRECVID 2007
Unconstrained Face Verification using Deep CNN Features
In this paper, we present an algorithm for unconstrained face verification
based on deep convolutional features and evaluate it on the newly released
IARPA Janus Benchmark A (IJB-A) dataset. The IJB-A dataset includes real-world
unconstrained faces from 500 subjects with full pose and illumination
variations which are much harder than the traditional Labeled Face in the Wild
(LFW) and Youtube Face (YTF) datasets. The deep convolutional neural network
(DCNN) is trained using the CASIA-WebFace dataset. Extensive experiments on the
IJB-A dataset are provided
A systematic review of data quality issues in knowledge discovery tasks
Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust
Dynamic Bayesian Combination of Multiple Imperfect Classifiers
Classifier combination methods need to make best use of the outputs of
multiple, imperfect classifiers to enable higher accuracy classifications. In
many situations, such as when human decisions need to be combined, the base
decisions can vary enormously in reliability. A Bayesian approach to such
uncertain combination allows us to infer the differences in performance between
individuals and to incorporate any available prior knowledge about their
abilities when training data is sparse. In this paper we explore Bayesian
classifier combination, using the computationally efficient framework of
variational Bayesian inference. We apply the approach to real data from a large
citizen science project, Galaxy Zoo Supernovae, and show that our method far
outperforms other established approaches to imperfect decision combination. We
go on to analyse the putative community structure of the decision makers, based
on their inferred decision making strategies, and show that natural groupings
are formed. Finally we present a dynamic Bayesian classifier combination
approach and investigate the changes in base classifier performance over time.Comment: 35 pages, 12 figure
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