35,561 research outputs found
Image Parsing with a Wide Range of Classes and Scene-Level Context
This paper presents a nonparametric scene parsing approach that improves the
overall accuracy, as well as the coverage of foreground classes in scene
images. We first improve the label likelihood estimates at superpixels by
merging likelihood scores from different probabilistic classifiers. This boosts
the classification performance and enriches the representation of
less-represented classes. Our second contribution consists of incorporating
semantic context in the parsing process through global label costs. Our method
does not rely on image retrieval sets but rather assigns a global likelihood
estimate to each label, which is plugged into the overall energy function. We
evaluate our system on two large-scale datasets, SIFTflow and LMSun. We achieve
state-of-the-art performance on the SIFTflow dataset and near-record results on
LMSun.Comment: Published at CVPR 2015, Computer Vision and Pattern Recognition
(CVPR), 2015 IEEE Conference o
Training Big Random Forests with Little Resources
Without access to large compute clusters, building random forests on large
datasets is still a challenging problem. This is, in particular, the case if
fully-grown trees are desired. We propose a simple yet effective framework that
allows to efficiently construct ensembles of huge trees for hundreds of
millions or even billions of training instances using a cheap desktop computer
with commodity hardware. The basic idea is to consider a multi-level
construction scheme, which builds top trees for small random subsets of the
available data and which subsequently distributes all training instances to the
top trees' leaves for further processing. While being conceptually simple, the
overall efficiency crucially depends on the particular implementation of the
different phases. The practical merits of our approach are demonstrated using
dense datasets with hundreds of millions of training instances.Comment: 9 pages, 9 Figure
Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
As a unique classification scheme for urban forms and functions, the local
climate zone (LCZ) system provides essential general information for any
studies related to urban environments, especially on a large scale. Remote
sensing data-based classification approaches are the key to large-scale mapping
and monitoring of LCZs. The potential of deep learning-based approaches is not
yet fully explored, even though advanced convolutional neural networks (CNNs)
continue to push the frontiers for various computer vision tasks. One reason is
that published studies are based on different datasets, usually at a regional
scale, which makes it impossible to fairly and consistently compare the
potential of different CNNs for real-world scenarios. This study is based on
the big So2Sat LCZ42 benchmark dataset dedicated to LCZ classification. Using
this dataset, we studied a range of CNNs of varying sizes. In addition, we
proposed a CNN to classify LCZs from Sentinel-2 images, Sen2LCZ-Net. Using this
base network, we propose fusing multi-level features using the extended
Sen2LCZ-Net-MF. With this proposed simple network architecture and the highly
competitive benchmark dataset, we obtain results that are better than those
obtained by the state-of-the-art CNNs, while requiring less computation with
fewer layers and parameters. Large-scale LCZ classification examples of
completely unseen areas are presented, demonstrating the potential of our
proposed Sen2LCZ-Net-MF as well as the So2Sat LCZ42 dataset. We also
intensively investigated the influence of network depth and width and the
effectiveness of the design choices made for Sen2LCZ-Net-MF. Our work will
provide important baselines for future CNN-based algorithm developments for
both LCZ classification and other urban land cover land use classification
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