scholarly journals Multi-scale classification of fluvial architecture: An example from the Palaeocene-Eocene Bighorn Basin, Wyoming

Sedimentology ◽  
2017 ◽  
Vol 64 (6) ◽  
pp. 1572-1596 ◽  
Author(s):  
Amanda Owen ◽  
Alena Ebinghaus ◽  
Adrian J. Hartley ◽  
Maurício G. M. Santos ◽  
Gary S. Weissmann
Keyword(s):  
Bighorn Basin ◽  
Multi Scale ◽  
Fluvial Architecture ◽  
Scale Classification

Multi-scale classification of disease using structural MRI and wavelet transform

NeuroImage ◽  
2012 ◽  
Vol 62 (1) ◽  
pp. 48-58 ◽  
Author(s):  
Kerstin Hackmack ◽  
Friedemann Paul ◽  
Martin Weygandt ◽  
Carsten Allefeld ◽  
John-Dylan Haynes
Keyword(s):  
Wavelet Transform ◽  
Structural Mri ◽  
Multi Scale ◽  
Scale Classification

Multi-Scale Classification Based on Remote Sensing

2014 ◽  
Vol 580-583 ◽  
pp. 2853-2859
Author(s):  
Peng Li Li ◽  
Wei Ping Ti ◽  
Jia Chun Li
Keyword(s):  
Remote Sensing ◽  
Multiple Scales ◽  
Know How ◽  
Multi Scale ◽  
Single Scale ◽  
The Difference ◽  
Feature Values ◽  
Nearest Neighbour Classifier ◽  
Scale Classification

Due to the broadly application of remote sensing imagery, there is an eager need for the classification of objects in the images. The multi-scale classification based on object oriented analysis is not a usual approach for image classification because the users of multi-scale classification do not know how to use the information from multiple scales to do multi-scale classification. Many users rely on some easily accessible tools. nearest neighbour classifier, to do multi-scale classification. The multi-scale classification classifies the images from different scales. The feature values of the object vary from different scales and they may have some trends against scales. These trends may help us to understand multi-scale classification better. This is the scale dependency of features. The difference between multi-scale classification and single-scale classification is not only multiple scales, but also the use of information from different scales. In order to explore the connection between different scales, the research of new features is necessary.

scholarly journals Deterministic Routing between Layout Abstractions for Multi-Scale Classification of Visually Rich Documents

2019 ◽  
Author(s):  
Ritesh Sarkhel ◽  
Arnab Nandi
Keyword(s):  
Turnaround Time ◽  
Convolutional Network ◽  
Multi Scale ◽  
Deterministic Routing ◽  
Pyramid Model ◽  
Benchmark Datasets ◽  
Work First ◽  
Spatial Pyramid ◽  
Scale Classification

Classifying heterogeneous visually rich documents is a challenging task. Difficulty of this task increases even more if the maximum allowed inference turnaround time is constrained by a threshold. The increased overhead in inference cost, compared to the limited gain in classification capabilities make current multi-scale approaches infeasible in such scenarios. There are two major contributions of this work. First, we propose a spatial pyramid model to extract highly discriminative multi-scale feature descriptors from a visually rich document by leveraging the inherent hierarchy of its layout. Second, we propose a deterministic routing scheme for accelerating end-to-end inference by utilizing the spatial pyramid model. A depth-wise separable multi-column convolutional network is developed to enable our method. We evaluated the proposed approach on four publicly available, benchmark datasets of visually rich documents. Results suggest that our proposed approach demonstrates robust performance compared to the state-of-the-art methods in both classification accuracy and total inference turnaround.

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