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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1991年第4期29卷 509-517页

作者： LEE, CH LANDGREBE, DA School of Electrical Engineering Purdue University West Lafayette IN USA

A multistage classification is proposed which reduces the processing time substantially. The proposed classification algorithm consists of several stages, and in each stage likelihood values of classes are calculated and compared. In the proposed multistage classification, if a class has a likelihood value less than a threshold, the class is truncated at that stage as an unlikely class, thus reducing the number of classes for which likelihood values are to be calculated at the next stage. Thus a host of classes can be truncated using a small portion of the total features at early stages, resulting in substantial reduction of computing time. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Experiments show that the proposed algorithm reduces the processing time by the factor of 3-7, depending on the number of classes and features, while maintaining essentially the same accuracies.

关键词： processing time Cutoffs Likelihood Classes sorting algorithm

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Online Learning a High-Quality Dictionary and Classifier Jointly for Multitask Object Tracking

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IEEE MULTIMEDIA 2014年第4期21卷 56-66页

作者： Fan, Baojie Gao, Hao Cong, Yang Du, Yingkui Tang, Yangdong Nanjing Univ Posts & Telecommun Nanjing Jiangsu Peoples R China Chinese Acad Sci Shenyang Inst Automat Beijing 100864 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing Jiangsu Peoples R China

Object tracking in a particle filter framework is formulated as a binary classification problem. The method exploits a priori information from training data to jointly learn a discriminative dictionary and optimal cla... 详细信息

关键词： Binary sequences Classification algorithms Encoding Learning systems Object tracking Online services Particle filters Research and development Target tracking discriminative dictionary learning label information multimedia multitask learning object tracking learning systems sorting algorithm Research and Development Information Services Target tracking binary sequences PARALLEL PROCESSING (COMPUTERS) tracking objects MULTI-MEDIA SYSTEM Particle filters Dictionaries as Topic Video sequences

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Multiscale fuel type mapping in fragmented ecosystems: preliminary results from hyperspectral MIVIS and multispectral Landsat TM data

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2006年第3期27卷 587-593页

作者： Lasaponara, R Lanorte, A Pignatti, S CNR Inst Methodol Environm Anal I-85050 Potenza Italy

This study aims to ascertain how well remote sensing data can characterize fuel type at different spatial scales in fragmented ecosystems. For this purpose, multisensor and multiscale remote sensing data such as hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) and Landsat Thematic Mapper (TM) data acquired in 1998 were analysed for a test area in southern Italy characterized by mixed vegetation covers and complex topography. Fieldwork fuel type recognition, performed at the same time as remote sensing data acquisitions, was used to assess the results obtained for the considered test areas. Results from preliminary analysis showed that the use of unmixing techniques allows an increase in accuracy of around 7% compared with the accuracy level obtained by applying a widely used classification algorithm.

关键词： TEST AREA remote-sensing data sorting algorithm spatial scale Fuel types Ecosystem thulium

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Structure of chain aggregates in ferrocolloids

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COLLOID JOURNAL 2003年第2期65卷 166-176页

作者： Ivanov, AO Kantorovich, SS Ural State Univ Ekaterinburg 620083 Russia

The structure of chain aggregates in polydisperse ferrocolloids is considered. The chain distribution over the number of constituent particles is investigated on the basis of the method of heterophase fluctuations. The polydispersity of ferrofluids is taken into account within the frameworks of two-fraction model suggesting the presence of a large number of small particles and a small number of large particles. The sorting algorithm of chain structures with various energies is presented. It is shown that, in real ferrofluids, the most probable are the chains composed of large particles with one or two small particles at the edges. The model predicts that the most of large particles are bonded into short chain aggregates whose average length consists of two or three particles at room temperature.

关键词： Ferrofluids sorting algorithm Frameworks CHAIN DISTRIBUTION mean length

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CART algorithm for spatial data: Application to environmental and ecological data

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第8期53卷 3082-3093页

作者： Bel, L. Allard, D. Laurent, J. M. Cheddadi, R. Bar-Hen, A. AgroParisTech INRA UMR 518 F-75231 Paris 05 France INRA Unite Biostat & Proc Spatiaux F-84914 Avignon France CNRS UMR 5554 Inst Sci Evolut F-34095 Montpellier France Univ Paris 05 UMR CNRS MAP5 8145 F-75270 Paris 06 France

Most statistical learning techniques such as Classification And Regression Trees (CART) assume independent samples to compute classification rules. This assumption is very practical for estimating quantities involved in the algorithm and for assessing asymptotic properties of estimators. In many environmental or ecological applications, the data under study are a sample of some regionalized variables, which can be modeled as random fields with spatial dependence. When the sampling scheme is very irregular, a direct application of supervised classification algorithms leads to biased discriminant rules due, for example, to the possible oversampling of some areas. The CART algorithm is adapted to the case of spatially dependent samples, focusing on environmental and ecological applications. Two approaches are considered. The first one takes into account the irregularity of the sampling by weighting the data according to their spatial pattern using two existing methods based on Voronoi tessellation and regular grid, and one original method based on kriging. The second one uses spatial estimates of the quantities involved in the construction of the discriminant rule at each step of the algorithm. These methods are tested on simulations and on a classical dataset to highlight their advantages and drawbacks. They are then applied on an ecological data set to explore the relationship between pollen data and presence/absence of tree species, which is an important question for climate reconstruction based on paleoecological data. (c) 2008 Elsevier B.V. All rights reserved.

关键词： REGRESSION TREES Rule sorting algorithm Dataset classification and regression trees Discriminant Dependent sample Ecological Applications asymptotic properties SPACE DEPENDENCE

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SPATIOTEMPORAL PATTERN-RECOGNITION USING HIDDEN MARKOV-MODELS

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1995年第4期31卷 1292-1300页

作者： FIELDING, KH RUCK, DW Air Force Institute of Technology USA

A spatio-temporal method for identifying objects contained in an image sequence is presented The Hidden Markov Model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. A five class problem is considered Classification accuracies of 100% and 99.7% are obtained for sequences of images generated over two separate regions of viewing positions. HMMs trained on image sequences of the objects moving in opposite directions showed a 98.1% successful classification rate by class and direction of movement. The HMM technique proved robust to image corruption with additive correlated noise and had a higher accuracy than a single-look nearest neighbor method. A real image sequence of one of the objects used was successfully recognized with the HMMs trained on synthetic data. This study shows the temporal changes that observed feature vectors undergo due to object motion hold information that can yield superior classification accuracy when compared with single-frame techniques.

关键词： feature extraction hidden Markov models image classification image sequences object detection additive correlated noise classification algorithm classification decisions image corruption image sequence object identification object mo sorting algorithm image sequences Image classification object recognition Object detection Feature extraction Hidden Markov models

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Class Specific Fuzzy Decision Trees for Mining High Speed Data Streams

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FUNDAMENTA INFORMATICAE 2008年第1-2期88卷 135-160页

作者： Hashemi, Sattar Kangavari, Mohammadreza Yang, Ying Iran Univ Sci & Technol Dept Comp Engn Tehran Iran Monash Univ Clayton Sch Informat Technol Clayton Vic 3800 Australia

In recent years, classification learning for data streams has become an important and active research topic. A major challenge posed by data streams is that their underlying concepts can change over time, which requires current classifiers to be revised accordingly and timely. To detect concept change, a common method is to observe the online classification accuracy. If accuracy drops below some threshold value, a concept change is deemed to have taken place. An implicit assumption behind this methodology is that any drop in accuracy can be interpreted as a symptom of concept change. Unfortunately however, this assumption is often violated in the real world where data streams carry noise and missing values that can also introduce a significant reduction in classification accuracy. To compound this problem, traditional noise cleansing methods are not applicable to data streams. These methods normally need to scan data multiple times whereas learning in data streams can only afford one-pass scan because of data's high speed and huge volume. To solve these problems, this paper proposes a novel classification algorithm, Class Specific Fuzzy Decision Trees (CSFDT), which utilizes fuzzy logic to classify data streams. The base classifier of CSFDT is a binary fuzzy decision tree. Whenever the problem of concern contains q classes (q > 2), CSFDT learns one binary classifier for each class to distinguish instances of this class from instances of the remaining (q - 1) classes. The CSFDT's advantages are three folds. First, it offers an adaptive structure to effectively and efficiently handle concept change. Second, it is robust to noise. Third, it deals with missing values in an elegant way. As a result, accuracy drop can be safely attributed to concept change. Extensive evaluations are conducted to compare CSFDT with representative existing data stream classification algorithms on a large variety of data. Experimental results suggest that CSFDT provides a significant benefit

关键词： sorting algorithm fuzzy decision tree on-line classification rivers data flow missing value streams high speed data Classifiers

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Phase Clustering Based Modulation Classification algorithm for PSK Signal over Wireless Environment

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MOBILE INFORMATION SYSTEMS 2016年第Pt.4期2016卷 2398464.1-2398464.1页

作者： An, Qi He, Zi-shu Li, Hui-yong Li, Yong-hua Univ Elect Sci & Technol China Dept Elect Engn Chengdu Peoples R China Air Force Airborne Acad Guilin Peoples R China

Promptitude and accuracy of signals' non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal's phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by themaximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power of Q. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of 10(-4) even when the SNR is as low as -15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.

关键词： Key technology Classifiers Phase shift keying baseband signal Supernova remnants sorting algorithm differential operation Fast Fourier transform FREQUENCY SHIFT signals Wireless networks signal to noise ratios PS-K Phase

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Inclusion of topographic variables in an unsupervised classification of satellite imagery

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CANADIAN JOURNAL OF REMOTE SENSING 2004年第2期30卷 137-149页

作者： Wulder, MA Franklin, SE White, JC Cranny, MM Dechka, JA Forestry Canada Pacific Forestry Ctr Nat Resources Canada Victoria BC V8Z 1M5 Canada Univ Saskatchewan Dept Geog Saskatoon SK S7N 5A2 Canada

Unsupervised classification has emerged as a common method for mapping the land cover of large areas with satellite data. Typically, clusters generated by an unsupervised algorithm, such as the K-means, are merged and labelled using a combination of manual and automated methods. Topographic shadows found in areas of high relief, particularly in areas with low sun angles, increase the complexity of land cover classification, as a single land cover class may have very different spectral responses in shadowed and nonshadowed areas of the image. Methods to increase classification accuracies in areas with severe topographic shadows are required for Canadian large area land cover mapping projects. In a standard supervised classification, increases in land cover map classification accuracy have been obtained by including topographic attributes as inputs to the classification algorithm. In this study we investigate the potential of such data as a means to increase the accuracy of an unsupervised land cover classification in a high-relief area in central British Columbia, Canada. Separate datasets were used in cluster labelling and accuracy assessment. Four classification trials were completed: (i) using a standard approach without the addition of topographic attributes, (ii) using elevation data as an additional input to (i), (iii) prestratifying the image into shadow and nonshadow areas prior to clustering, and (h) combining the methods used in (ii) and (iii). The latter provided the highest level of overall attribute accuracy at 80.1%, with a 95% confidence interval of 73.6%-88.6%. This is an improvement over the standard approach, which produced an overall attribute accuracy of 68.7%, with a 95% confidence interval of 59.8%-77.6%. We concluded that the prestratification of an image into areas of shadow and nonshadow prior to clustering in conjunction with the use of elevation data as an input to the clustering process is a practical method to increase classification accu

关键词： Educated at mainstream school Taxonomy land cover Confidence intervals images high relief Satellite Imagery clustering process shadows sorting algorithm

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Kinetic filtering of [¹⁸F]Fluorothymidine in positron emission tomography studies

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PHYSICS IN MEDICINE AND BIOLOGY 2010年第3期55卷 695-709页

作者： Gray, Katherine R. Contractor, Kaiyumars B. Kenny, Laura M. Al-Nahhas, Adil Shousha, Sami Stebbing, Justin Wasan, Harpreet S. Coombes, R. Charles Aboagye, Eric O. Turkheimer, Federico E. Rosso, Lula Univ London Imperial Coll Sci Technol & Med Hammersmith Hosp Ctr Clin Sci MRC London England Univ London Imperial Coll Sci Technol & Med Hammersmith Hosp Fac Med Dept Oncol London England Hammersmith Hosp Imperial Coll NHS Trust Dept Nucl Med London England Charing Cross Hosp Imperial Coll NHS Trust Dept Pathol London England Univ London Imperial Coll Sci Technol & Med Hammersmith Hosp Fac Med Div Neurosci & Mental Hlth London England

[F-18]Fluorothymidine (FLT) is a cell proliferation marker that undergoes predominantly hepatic metabolism and therefore shows a high level of accumulation in the liver, as well as in rapidly proliferating tumours. Furthermore, the tracer's uptake is substantial in other organs including the heart. We present a nonlinear kinetic filtering technique which enhances the visualization of tumours imaged with FLT positron emission tomography (FLT-PET). A classification algorithm to isolate cancerous tissue from healthy organs was developed and validated using 29 scan data from patients with locally advanced or metastatic breast cancer. A large reduction in signal from the liver and heart of 80% was observed following application of the kinetic filter, whilst the majority of signal from both primary tumours and metastases was retained. A scan acquisition time of 60 min has been shown to be sufficient to obtain the necessary kinetic data. The algorithm extends utility of FLT-PET imaging in oncology research.

关键词： sorting algorithm FLT Visualization PROLIFERATION MARKER metastasis Heart PET Scan Cancer Kinetics Tumours Liver Filtration

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