Augmenting Deep Learning Performance in an Evidential Multiple Classifier System

International audienceThe main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier's respective strengths, we combine them in an evidential framework, which models specifically their imprecision and uncertainty. The application we consider in order to illustrate the interest of our Multiple Classifier System is pedestrian detection in high-density crowds, which is ideally suited for its difficulty, cost of labeling and intrinsic imprecision of annotation data. We show that the fusion resulting from the effective modeling of uncertainty allows for performance improvement, and at the same time, for a deeper interpretation of the result in terms of commitment of the decision

Combination of partially non-distinct beliefs: The cautious-adaptive rule

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Combination of partially non-distinct beliefs: The cautious-adaptive rule

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Application of Shannon Information Theory to a Comparison between L- and C-Band SIR-C Polarimetric Data versus Incidence Angle

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Surface soil moisture estimation from the synergistic use of the (multi-incidence and multi-resolution) active microwave ERS Wind Scatterometer and SAR data

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Automatic change detection by evidential fusion of change indices.

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Evidential framework for Error Correcting Output Code classification

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A contour-based approach for clods identification and characterization on a soil surface

International audienceBecause the surface micro-topography has a large impact on soil properties, numerous studies have focused on surface roughness, soil height changes, and soil cloddiness characterization. Usually, feature parameters are estimated from soil measurement samples, based on statistics characterizing the surface as a whole. Now, the shortcoming of such a global approach is that it fails to detect local soil height changes and non-stationarities. The present study introduces a new method to identify and characterize the clods on a seedbed surface Digital Elevation Model (DEM). It is based on an a priori model of the clods, namely objects presenting closed elevation contours with high gradient values. Our clod segmentation method was assessed with the help of a soil scientist on the two kinds of tilled soil surfaces which were considered in this study: an artificial surface made in the laboratory to have a controlled roughness, and an actual seedbed surface made in an experimental field. In both cases results were evaluated in terms of sensitivity and specificity, and showed the performance of the method. We also study the impacts of the main parameters of the method and the computer time. Its main limitations are that it fails to identify the small clods (diameter smaller than 7 mm in this study) and the clods embedded within another piece of relief, such as a greater clod or a hollow border. Then, we show how results can be used to compute clod parameters: mapping of clods provided as output of our algorithm and clod shape measurements. Finally, an application to study soil heights changes with rainfall is provided