Mining data with random forests: A survey and results of new tests

Deep learning approach for active classification of electrocardiogram signals

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Active Learning Methods for Remote Sensing Image Classification

The goal of active learning is to select the most informative examples for manual labeling. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient since the classification model has to be retrained for every labeled example. In this paper, we present a framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously. The key computational challenge is how to efficiently identify the subset of unlabeled examples that can result in the largest reduction in the Fisher information. To resolve this challenge, we propose an efficient greedy algorithm that is based on the property of submodular functions. Our empirical studies with five UCI datasets and one real-world medical image classification show that the proposed batch mode active learning algorithm is more effective than the state-of-the-art algorithms for active learning.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3389&context=sis_research

Batch mode active learning and its application to medical image classification

High-resolution photomicrographs of phytoplankton cells and chains can now be acquired with imaging-in-flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom-built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co-occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22-category classifier provides 88% overall accuracy for an independent test set, with individual category accuracies ranging from 68% to 99%. We demonstrate application of this classifier to a nearly uninterrupted 2-month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.

https://aslopubs.onlinelibrary.wiley.com/doi/pdf/10.4319/lom.2007.5.204

Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry

Active learning has been applied with support vector machines to reduce the data labeling effort in pattern recognition domains. However, most of those applications only deal with two class problems. In this paper, we extend the active learning approach to multiple class support vector machines. The experimental results from a plankton recognition system indicate that our approach often requires significantly less labeled images to maintain the same accuracy level as random sampling.

/pdf/active-learning-to-recognize-multiple-types-of-plankton-1w3wowmf5w.pdf

Active learning to recognize multiple types of plankton

We discuss an in situ marine imaging system based on high-speed digital line scan cameras for collection of a continuous picture of microscopic marine particles ranging in size from 200 /spl mu/m to several centimeters The system is built to operate on a 53-cm-diameter autonomous underwater vehicle or a tethered platform The digital imaging system provides a continuous record of all particles passing through a symmetric 96/spl times/96 mm sampling tube and provides views from two orthogonal directions Data are compressed using a lossless encoding technique and stored onto a disk drive Over 50 h of continuous imaging is possible using the system Data are suitable for studies requiring sizing, identification, quantification, and spatial recording of semi-transparent and opaque particles This paper summarizes the mechanical, optical, and data processing design of this instrument and discusses recent improvements We also present images and quantitative results from recent deployments in the Gulf of Mexico

A system for high-resolution zooplankton imaging

: The goal of this project is to develop a broadly-capable software package, to automatically classify digital images of zooplankton. The software is being developed initially for classification of images from the SIPPER linescan imaging instrument, but will also be able to process images from imaging systems that produce high quality digital image. Towards this end, the software is being created to include both training and multi-stage classification portions. The input is digital images in standard computer format. The output is an Internet-addressable classified particle database, including pertinent particle hydrographic information and sorted images. The project has application in rapid classification of microscopic marine particles, which have an effect on the optical properties and long-term variation in the local and global water column.

/pdf/development-of-automated-image-analysis-software-for-90kpozdqo1.pdf

Development of Automated Image Analysis Software for Suspended Marine Particle Classification

: The long term goal is development and utilization of a comprehensive (broadly capable) marine particle analysis system The system is designed with wide dynamic range, thus, it will ultimately be used for high speed, high resolution characterization of water column particle fields in high, medium and low latitudes As part of a broader goal this project continues advancement of the AOSN concept through development of both towed platforms and autonomous underwater vehicles

Comprehensive Marine Particle Analysis System

: The long term goal is development and utilization of a suite of high-resolution, linescan-camera based imaging systems to investigate the distribution and composition of plankton and suspended particles in the marine environment. The system is designed with wide dynamic range and will ultimately be used for high speed, high resolution characterization of water column particle fields in high, medium and low latitudes. Such a system will have broad application to areas of defense and environmental interests such as ecological modeling efforts, Autonomous Ocean Sampling Networks (AOSN), and ground-truthing of satellite data.

/pdf/development-and-field-application-of-laser-particle-imagers-4bs2mubfw4.pdf

Development and Field Application of Laser Particle Imagers

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