237,065 research outputs found
Improving Image Classification with Location Context
With the widespread availability of cellphones and cameras that have GPS
capabilities, it is common for images being uploaded to the Internet today to
have GPS coordinates associated with them. In addition to research that tries
to predict GPS coordinates from visual features, this also opens up the door to
problems that are conditioned on the availability of GPS coordinates. In this
work, we tackle the problem of performing image classification with location
context, in which we are given the GPS coordinates for images in both the train
and test phases. We explore different ways of encoding and extracting features
from the GPS coordinates, and show how to naturally incorporate these features
into a Convolutional Neural Network (CNN), the current state-of-the-art for
most image classification and recognition problems. We also show how it is
possible to simultaneously learn the optimal pooling radii for a subset of our
features within the CNN framework. To evaluate our model and to help promote
research in this area, we identify a set of location-sensitive concepts and
annotate a subset of the Yahoo Flickr Creative Commons 100M dataset that has
GPS coordinates with these concepts, which we make publicly available. By
leveraging location context, we are able to achieve almost a 7% gain in mean
average precision
Context-self contrastive pretraining for crop type semantic segmentation
In this paper, we propose a fully supervised pre-training scheme based on
contrastive learning particularly tailored to dense classification tasks. The
proposed Context-Self Contrastive Loss (CSCL) learns an embedding space that
makes semantic boundaries pop-up by use of a similarity metric between every
location in a training sample and its local context. For crop type semantic
segmentation from Satellite Image Time Series (SITS) we find performance at
parcel boundaries to be a critical bottleneck and explain how CSCL tackles the
underlying cause of that problem, improving the state-of-the-art performance in
this task. Additionally, using images from the Sentinel-2 (S2) satellite
missions we compile the largest, to our knowledge, SITS dataset densely
annotated by crop type and parcel identities, which we make publicly available
together with the data generation pipeline. Using that data we find CSCL, even
with minimal pre-training, to improve all respective baselines and present a
process for semantic segmentation at super-resolution for obtaining crop
classes at a more granular level. The code and instructions to download the
data can be found in https://github.com/michaeltrs/DeepSatModels.Comment: 15 pages, 17 figure
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Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.
The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks
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