28,067 research outputs found
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification
We present an exhaustive investigation of recent Deep Learning architectures,
algorithms, and strategies for the task of document image classification to
finally reduce the error by more than half. Existing approaches, such as the
DeepDocClassifier, apply standard Convolutional Network architectures with
transfer learning from the object recognition domain. The contribution of the
paper is threefold: First, it investigates recently introduced very deep neural
network architectures (GoogLeNet, VGG, ResNet) using transfer learning (from
real images). Second, it proposes transfer learning from a huge set of document
images, i.e. 400,000 documents. Third, it analyzes the impact of the amount of
training data (document images) and other parameters to the classification
abilities. We use two datasets, the Tobacco-3482 and the large-scale RVL-CDIP
dataset. We achieve an accuracy of 91.13% for the Tobacco-3482 dataset while
earlier approaches reach only 77.6%. Thus, a relative error reduction of more
than 60% is achieved. For the large dataset RVL-CDIP, an accuracy of 90.97% is
achieved, corresponding to a relative error reduction of 11.5%
Structure Preserving Large Imagery Reconstruction
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as image clustering, 3D scene
reconstruction, and other big data applications. However, such tasks are not
easy due to the fact the retrieved photos can have large variations in their
view perspectives, resolutions, lighting, noises, and distortions.
Fur-thermore, with the occlusion of unexpected objects like people, vehicles,
it is even more challenging to find feature correspondences and reconstruct
re-alistic scenes. In this paper, we propose a structure-based image completion
algorithm for object removal that produces visually plausible content with
consistent structure and scene texture. We use an edge matching technique to
infer the potential structure of the unknown region. Driven by the estimated
structure, texture synthesis is performed automatically along the estimated
curves. We evaluate the proposed method on different types of images: from
highly structured indoor environment to natural scenes. Our experimental
results demonstrate satisfactory performance that can be potentially used for
subsequent big data processing, such as image localization, object retrieval,
and scene reconstruction. Our experiments show that this approach achieves
favorable results that outperform existing state-of-the-art techniques
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