6,555 research outputs found
Content-Based Video Retrieval in Historical Collections of the German Broadcasting Archive
The German Broadcasting Archive (DRA) maintains the cultural heritage of
radio and television broadcasts of the former German Democratic Republic (GDR).
The uniqueness and importance of the video material stimulates a large
scientific interest in the video content. In this paper, we present an
automatic video analysis and retrieval system for searching in historical
collections of GDR television recordings. It consists of video analysis
algorithms for shot boundary detection, concept classification, person
recognition, text recognition and similarity search. The performance of the
system is evaluated from a technical and an archival perspective on 2,500 hours
of GDR television recordings.Comment: TPDL 2016, Hannover, Germany. Final version is available at Springer
via DO
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Rich high-quality annotated data is critical for semantic segmentation
learning, yet acquiring dense and pixel-wise ground-truth is both labor- and
time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an
economical alternative, with which training phase could hardly generate
satisfactory performance unfortunately. In order to generate high-quality
annotated data with a low time cost for accurate segmentation, in this paper,
we propose a novel annotation enrichment strategy, which expands existing
coarse annotations of training data to a finer scale. Extensive experiments on
the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural
networks trained with the enriched annotations from our framework yield a
significant improvement over that trained with the original coarse labels. It
is highly competitive to the performance obtained by using human annotated
dense annotations. The proposed method also outperforms among other
state-of-the-art weakly-supervised segmentation methods.Comment: CIKM 2018 International Conference on Information and Knowledge
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