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Academic literature on the topic 'Semi-automatic classification'

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Published: 4 June 2021

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Journal articles on the topic "Semi-automatic classification"

Gurulingappa, Harsha, Corinna Kolářik, Martin Hofmann-Apitius, and Juliane Fluck. "Concept-Based Semi-Automatic Classification of Drugs." Journal of Chemical Information and Modeling 49, no. 8 (August 10, 2009): 1986–92. http://dx.doi.org/10.1021/ci9000844.

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Lee, Won-Goo, Sung-Ho Shin, Kwang-Young Kim, Do-Heon Jeon, Hwa-Mook Yoon, Won-Kyung Sung, and Min-Ho Lee. "Semi-Automatic Management of Classification Scheme with Interoperability." Journal of the Korea Contents Association 11, no. 12 (December 28, 2011): 466–74. http://dx.doi.org/10.5392/jkca.2011.11.12.466.

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Yang, Jie, and Xianzhong Zhou. "Semi-automatic Web Service Classification Using Machine Learning." International Journal of u- and e-Service, Science and Technology 8, no. 4 (April 30, 2015): 339–48. http://dx.doi.org/10.14257/ijunesst.2015.8.4.31.

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Kockelkorn, Thessa T. J. P., Pim A. de Jong, Cornelia M. Schaefer-Prokop, Rianne Wittenberg, Audrey M. Tiehuis, Hester A. Gietema, Jan C. Grutters, Max A. Viergever, and Bram van Ginneken. "Semi-automatic classification of textures in thoracic CT scans." Physics in Medicine and Biology 61, no. 16 (July 20, 2016): 5906–24. http://dx.doi.org/10.1088/0031-9155/61/16/5906.

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Chen, Yung-Fu, Po-Chi Huang, Ker-Cheng Lin, Hsuan-Hung Lin, Li-En Wang, Chung-Chuan Cheng, Tsung-Po Chen, Yung-Kuan Chan, and John Y. Chiang. "Semi-Automatic Segmentation and Classification of Pap Smear Cells." IEEE Journal of Biomedical and Health Informatics 18, no. 1 (January 2014): 94–108. http://dx.doi.org/10.1109/jbhi.2013.2250984.

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Pereira, Tiago Rafael de Barros, Helenice Vital, André Giskard Aquino da Silva, and Cecília Alves de Oliveira. "COMPARISON OF AUTOMATIC AND SEMI-AUTOMATIC METHODS FOR CLASSIFICATION OF SIDE SCAN SONAR IMAGERY." Brazilian Journal of Geophysics 37, no. 1 (March 27, 2019): 1. http://dx.doi.org/10.22564/rbgf.v37i1.1985.

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ABSTRACT. The main scope of this paper is the analysis of seafloor classification using acoustic remote sensing data. These data were acquired in a hydroacoustic survey of bathymetry and sonography using an interferometric swath bathymetry system. The study area is a sector of the internal northeast Brazilian shelf adjacent to the Ponta Negra beach - Natal (RN). The objective of the work is to identify and draw the different textural patterns, which characterize the seafloor of the study area. In addition, two approaches for textural classification of sonograms were compared and evaluated, which were: Automatic Gray Level Co-occurrence Matrix (GLCM) classification available in SonarWiz software; and the semi-automatic Maximum Likelihood, available in ArcGIS software. The comparison tested the capacity for identifying and drawing the textural patterns distribution. The automated classification identified 4 patterns while on the semi-automated 5 patterns were identified. It was made the correlation between the textural patterns found in each classification, besides the correlation between textural patterns and the levels of intensity of reflectance presents on the sonogram.Keywords: sonography, textural classification, textural patterns, hydroacoustic. RESUMO. Este trabalho foi realizado a partir da análise de dados geofísicos adquiridos em levantamento hidroacústico de batimetria e sonografia utilizando um sonar interferométrico EdgeTech 4600. A área de estudo é uma porção da plataforma interna do nordeste brasileiro adjacente Natal (RN). O objetivo deste trabalho é identificar e delimitar os diferentes padrões texturais que caracterizam o substrato marinho da área de estudo. Adicionalmente, são avaliadas e comparadas duas abordagens distintas de classificação textural de sonogramas, sendo elas: a classificação automática GLCM disponível no software SonarWiz, e a classificação semi-automática máxima verossimilhança (Maximum Likelihood) disponível no software ArcGIS. A comparação foi realizada com base na capacidade de identificação e delimitação da distribuição dos padrões texturais. A utilização da classificação automática identificou 4 padrões, enquanto que, na classificação semi-automática 6 padrões foram identificados. Foi feita a correlação entre os padrões texturais encontrados em cada classificação, além da correlação entre os padrões texturais e os níveis de intensidade de reflectância presente no sonograma.Palavras-chave: sonografia, classificação textural, padrões de textura, hidroacústica.

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Jenerowicz, M., and M. Banaszkiewicz. "ASTEROID (21) LUTETIA: SEMI-AUTOMATIC IMPACT CRATERS DETECTION AND CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 479–86. http://dx.doi.org/10.5194/isprs-archives-xlii-2-479-2018.

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The need to develop an automated method, independent of lighting and surface conditions, for the identification and measurement of impact craters, as well as the creation of a reliable and efficient tool, has become a justification of our studies. This paper presents a methodology for the detection of impact craters based on their spectral and spatial features. The analysis aims at evaluation of the algorithm capabilities to determinate the spatial parameters of impact craters presented in a time series. In this way, time-consuming visual interpretation of images would be reduced to the special cases. The developed algorithm is tested on a set of OSIRIS high resolution images of asteroid Lutetia surface which is characterized by varied landforms and the abundance of craters created by collisions with smaller bodies of the solar system.The proposed methodology consists of three main steps: characterisation of objects of interest on limited set of data, semi-automatic extraction of impact craters performed for total set of data by applying the Mathematical Morphology image processing (Serra, 1988, Soille, 2003), and finally, creating libraries of spatial and spectral parameters for extracted impact craters, i.e. the coordinates of the crater center, semi-major and semi-minor axis, shadow length and cross-section. The overall accuracy of the proposed method is 98&thinsp;%, the Kappa coefficient is 0.84, the correlation coefficient is &sim;&thinsp;0.80, the omission error 24.11&thinsp;%, the commission error 3.45&thinsp;%. The obtained results show that methods based on Mathematical Morphology operators are effective also with a limited number of data and low-contrast images.

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Chen, Zhixin, and Robert C. Maher. "Semi-automatic classification of bird vocalizations using spectral peak tracks." Journal of the Acoustical Society of America 120, no. 5 (November 2006): 2974–84. http://dx.doi.org/10.1121/1.2345831.

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Wong, M. L. Dennis, and Asoke K. Nandi. "Semi-blind algorithms for automatic classification of digital modulation schemes." Digital Signal Processing 18, no. 2 (March 2008): 209–27. http://dx.doi.org/10.1016/j.dsp.2007.02.007.

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Duangsuwan, Jarunee, and Pawin Saeku. "Semi-automatic Classification Based on ICD Code for Thai Text-Based Chief Complaint by Machine Learning Techniques." International Journal of Future Computer and Communication 7, no. 2 (June 2018): 37–41. http://dx.doi.org/10.18178/ijfcc.2018.7.2.517.

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Dissertations / Theses on the topic "Semi-automatic classification"

NUNES, BERNARDO PEREIRA. "AUTOMATIC CLASSIFICATION OF SEMI-STRUCTURED DATA." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14382@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
O problema da classificação de dados remonta à criação de taxonomias visando cobrir áreas do conhecimento. Com o surgimento da Web, o volume de dados disponíveis aumentou várias ordens de magnitude, tornando praticamente impossível a organização de dados manualmente. Esta dissertação tem por objetivo organizar dados semi-estruturados, representados por frames, sem uma estrutura de classes prévia. A dissertação apresenta um algoritmo, baseado no K-Medóide, capaz de organizar um conjunto de frames em classes, estruturadas sob forma de uma hierarquia estrita. A classificação dos frames é feita a partir de um critério de proximidade que leva em conta os atributos e valores que cada frame possui.
The problem of data classification goes back to the definition of taxonomies covering knowledge areas. With the advent of the Web, the amount of data available has increased several orders of magnitude, making manual data classification impossible. This dissertation proposes a method to automatically classify semi-structured data, represented by frames, without any previous knowledge about structured classes. The dissertation introduces an algorithm, based on K-Medoid, capable of organizing a set of frames into classes, structured as a strict hierarchy. The classification of the frames is based on a closeness criterion that takes into account the attributes and their values in each frame.

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Dos, santos Jefersson Alex. "Semi-automatic Classification of Remote Sensing Images." Phd thesis, Université de Cergy Pontoise, 2013. http://tel.archives-ouvertes.fr/tel-00878612.

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A huge effort has been made in the development of image classification systemswith the objective of creating high-quality thematic maps and to establishprecise inventories about land cover use. The peculiarities of Remote SensingImages (RSIs) combined with the traditional image classification challengesmake RSI classification a hard task. Many of the problems are related to therepresentation scale of the data, and to both the size and therepresentativeness of used training set.In this work, we addressed four research issues in order to develop effectivesolutions for interactive classification of remote sensing images.The first research issue concerns the fact that image descriptorsproposed in the literature achieve good results in various applications, butmany of them have never been used in remote sensing classification tasks.We have tested twelve descriptors that encodespectral/color properties and seven texture descriptors. We have also proposeda methodology based on the K-Nearest Neighbor (KNN) classifier for evaluationof descriptors in classification context. Experiments demonstrate that JointAuto-Correlogram (JAC), Color Bitmap, Invariant Steerable Pyramid Decomposition(SID), and Quantized Compound Change Histogram (QCCH) yield the best results incoffee and pasture recognition tasks.The second research issue refers to the problem of selecting the scaleof segmentation for object-based remote sensing classification. Recentlyproposed methods exploit features extracted from segmented objects to improvehigh-resolution image classification. However, the definition of the scale ofsegmentation is a challenging task. We have proposedtwo multiscale classification approaches based on boosting of weak classifiers.The first approach, Multiscale Classifier (MSC), builds a strongclassifier that combines features extracted from multiple scales ofsegmentation. The other, Hierarchical Multiscale Classifier (HMSC), exploits thehierarchical topology of segmented regions to improve training efficiencywithout accuracy loss when compared to the MSC. Experiments show that it isbetter to use multiple scales than use only one segmentation scale result. Wehave also analyzed and discussed about the correlation among the useddescriptors and the scales of segmentation.The third research issue concerns the selection of training examples and therefinement of classification results through multiscale segmentation. We have proposed an approach forinteractive multiscale classification of remote sensing images.It is an active learning strategy that allows the classification resultrefinement by the user along iterations. Experimentalresults show that the combination of scales produces better results thanisolated scales in a relevance feedback process. Furthermore, the interactivemethod achieves good results with few user interactions. The proposed methodneeds only a small portion of the training set to build classifiers that are asstrong as the ones generated by a supervised method that uses the whole availabletraining set.The fourth research issue refers to the problem of extracting features of ahierarchy of regions for multiscale classification. We have proposed a strategythat exploits the existing relationships among regions in a hierarchy. Thisapproach, called BoW-Propagation, exploits the bag-of-visual-word model topropagate features along multiple scales. We also extend this idea topropagate histogram-based global descriptors, the H-Propagation method. The proposedmethods speed up the feature extraction process and yield good results when compared with globallow-level extraction approaches.

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Santos, Jefersson Alex dos 1984. "Semi-automatic classification of remote sensing images = Classificação semi-automática de imagens de sensorimento remoto." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275630.

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Orientadores: Ricardo da Silva Torres, Alexandre Xavier Falcão
Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-23T15:18:27Z (GMT). No. of bitstreams: 1 Santos_JeferssonAlexdos_D.pdf: 18672412 bytes, checksum: 58ac60d8b5342ab705a78d5c82265ab8 (MD5) Previous issue date: 2013
Resumo: Um grande esforço tem sido feito para desenvolver sistemas de classificação de imagens capazes de criar mapas temáticos de alta qualidade e estabelecer inventários precisos sobre o uso do solo. As peculiaridades das imagens de sensoriamento remoto (ISR), combinados com os desafios tradicionais de classificação de imagens, tornam a classificação de ISRs uma tarefa difícil. Grande parte dos desafios de pesquisa estão relacionados à escala de representação dos dados e, ao mesmo tempo, à dimensão e à representatividade do conjunto de treinamento utilizado. O principal foco desse trabalho está nos problemas relacionados à representação dos dados e à extração de características. O objetivo é desenvolver soluções efetivas para classificação interativa de imagens de sensoriamento remoto. Esse objetivo foi alcançado a partir do desenvolvimento de quatro linhas de pesquisa. A primeira linha de pesquisa está relacionada ao fato de embora descritores de imagens propostos na literatura obterem bons resultados em várias aplicações, muitos deles nunca foram usados para classificação de imagens de sensoriamento remoto. Nessa tese, foram testados doze descritores que codificam propriedades espectrais e sete descritores de textura. Também foi proposta uma metodologia baseada no classificador K-Vizinhos mais Próximos (K-nearest neighbors - KNN) para avaliação de descritores no contexto de classificação. Os descritores Joint Auto-Correlogram (JAC), Color Bitmap, Invariant Steerable Pyramid Decomposition (SID) e Quantized Compound Change Histogram (QCCH), apresentaram os melhores resultados experimentais na identificação de alvos de café e pastagem. A segunda linha de pesquisa se refere ao problema de seleção de escalas de segmentação para classificação de imagens de sensoriamento baseada em objetos. Métodos propostos recentemente exploram características extraídas de objetos segmentados para melhorar a classificação de imagens de alta resolução. Entretanto, definir uma escala de segmentação adequada é uma tarefa desafiadora. Nessa tese, foram propostas duas abordagens de classificação multiescala baseadas no algoritmo Adaboost. A primeira abordagem, Multiscale Classifier (MSC), constrói um classificador forte que combina características extraídas de múltiplas escalas de segmentação. A outra, Hierarchical Multiscale Classifier (HMSC), explora a relação hierárquica das regiões segmentadas para melhorar a eficiência sem reduzir a qualidade da classificação xi quando comparada à abordagem MSC. Os experimentos realizados mostram que é melhor usar múltiplas escalas do que utilizar apenas uma escala de segmentação. A correlação entre os descritores e as escalas de segmentação também é analisada e discutida. A terceira linha de pesquisa trata da seleção de amostras de treinamento e do refinamento dos resultados da classificação utilizando segmentação multiescala. Para isso, foi proposto um método interativo para classificação multiescala de imagens de sensoriamento remoto. Esse método utiliza uma estratégia baseada em aprendizado ativo que permite o refinamento dos resultados de classificação pelo usuário ao longo de interações. Os resultados experimentais mostraram que a combinação de escalas produzem melhores resultados do que a utilização de escalas isoladas em um processo de realimentação de relevância. Além disso, o método interativo obtém bons resultados com poucas interações. O método proposto necessita apenas de uma pequena porção do conjunto de treinamento para construir classificadores tão fortes quanto os gerados por um método supervisionado utilizando todo o conjunto de treinamento disponível. A quarta linha de pesquisa se refere à extração de características de uma hierarquia de regiões para classificação multiescala. Assim, foi proposta uma abordagem que explora as relações existentes entre as regiões da hierarquia. Essa abordagem, chamada BoW-Propagation, utiliza o modelo bag-of-visual-word para propagar características ao longo de múltiplas escalas. Essa ideia foi estendida para propagar descritores globais baseados em histogramas, a abordagem H-Propagation. As abordagens propostas aceleram o processo de extração e obtém bons resultados quando comparadas a descritores globais
Abstract: A huge effort has been made in the development of image classification systems with the objective of creating high-quality thematic maps and to establish precise inventories about land cover use. The peculiarities of Remote Sensing Images (RSIs) combined with the traditional image classification challenges make RSI classification a hard task. Many of the problems are related to the representation scale of the data, and to both the size and the representativeness of used training set. In this work, we addressed four research issues in order to develop effective solutions for interactive classification of remote sensing images. The first research issue concerns the fact that image descriptors proposed in the literature achieve good results in various applications, but many of them have never been used in remote sensing classification tasks. We have tested twelve descriptors that encode spectral/color properties and seven texture descriptors. We have also proposed a methodology based on the K-Nearest Neighbor (KNN) classifier for evaluation of descriptors in classification context. Experiments demonstrate that Joint Auto-Correlogram (JAC), Color Bitmap, Invariant Steerable Pyramid Decomposition (SID), and Quantized Compound Change Histogram (QCCH) yield the best results in coffee and pasture recognition tasks. The second research issue refers to the problem of selecting the scale of segmentation for object-based remote sensing classification. Recently proposed methods exploit features extracted from segmented objects to improve high-resolution image classification. However, the definition of the scale of segmentation is a challenging task. We have proposed two multiscale classification approaches based on boosting of weak classifiers. The first approach, Multiscale Classifier (MSC), builds a strong classifier that combines features extracted from multiple scales of segmentation. The other, Hierarchical Multiscale Classifier (HMSC), exploits the hierarchical topology of segmented regions to improve training efficiency without accuracy loss when compared to the MSC. Experiments show that it is better to use multiple scales than use only one segmentation scale result. We have also analyzed and discussed about the correlation among the used descriptors and the scales of segmentation. The third research issue concerns the selection of training examples and the refinement of classification results through multiscale segmentation. We have proposed an approach for xix interactive multiscale classification of remote sensing images. It is an active learning strategy that allows the classification result refinement by the user along iterations. Experimental results show that the combination of scales produces better results than isolated scales in a relevance feedback process. Furthermore, the interactive method achieves good results with few user interactions. The proposed method needs only a small portion of the training set to build classifiers that are as strong as the ones generated by a supervised method that uses the whole available training set. The fourth research issue refers to the problem of extracting features of a hierarchy of regions for multiscale classification. We have proposed a strategy that exploits the existing relationships among regions in a hierarchy. This approach, called BoW-Propagation, exploits the bag-of-visual-word model to propagate features along multiple scales. We also extend this idea to propagate histogram-based global descriptors, the H-Propagation method. The proposed methods speed up the feature extraction process and yield good results when compared with global low-level extraction approaches
Doutorado
Ciência da Computação
Doutor em Ciência da Computação

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Trias-Sanz, Roger. "Semi-automatic rural land cover classification from high resolution remote sensing images." Paris 5, 2006. http://www.theses.fr/2006PA05S005.

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Cette thèse présente un chaine d'analyse d'image qui, à partir d'images numériques à haute résolution et à trois ou quatre canaux (50 cm, couleur et, dans certains cas, proche infrarouge), mais aussi en s'appuyant sur le parcellaire cadastral, rend une segmentation des images en parcelles agraires (champs, forêts, vignes,. . . ) et une classification de celles-ci, avec une très haute fiabilité, et attribue à chaque segment classifié une mesure qui indique la confiance que le système a en cette classification. Elle inclut une étude sur l'intérêt de la texture et les espaces de couleur pour la segmentation et la classification, deux méthodes de recalage de graphes sur une image, un modèle de probabilité novateur et ses algorthmes de classification par régions associées, et un éstimateur très précis de la période et de l'orientation
This thesis presents a complete image analisys system which, from high-resolution 3 or 4-channel digital images (50 cm, colour and optionally near infrared), and using the cadastre database, segments the images into agriculturally-homogeneous regions, (fields, forests, vines, and so on) and classifies these regions, tagging each classified region with a confidence measure which indicates the system's confidence in each classification. It includes a study of the value of texture features and transformed colour spaces for segmentation and classification, two methods for registering a graph onto an image, a novel probability model and associated per-region classification algorithms, and a high precision period and orientation estimator

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Melo, Claudia de Oliveira. "Classificação semi-automática de componentes Java." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-06042009-214829/.

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As recentes tecnologias de desenvolvimento e distribuição de componentes possibilitaram o aumento do número de componentes disponíveis no mercado. No entanto, eles muitas vezes estão dispersos e não publicados adequadamente para a comunidade de pesquisa e desenvolvimento de software. Encontrar componentes apropriados para solucionar um problema particular não é uma tarefa simples e novas técnicas devem ser desenvolvidas para o reuso efetivo de componentes. Um dos maiores desafios em reusar componentes consiste em classificá-los corretamente para futuras consultas. Classificar componentes para possibilitar uma busca eficaz depende da qualidade das informações adquiridas, que viabilizam melhor precisão e cobertura das consultas ao encontrar componentes reutilizáveis em potencial. Ao mesmo tempo, mecanismos de classificação e busca devem ser fáceis o suficiente para convencer os desenvolvedores a reusar componentes. Este trabalho estuda as técnicas de classificação de componentes de software, repositórios e métodos de busca. é apresentada uma proposta de modelo de classificação de componentes que considera não apenas sua função, mas o negócio onde ele está inserido e seus atributos de qualidade. Um método de preenchimento semi-automático das informações é proposto, de modo a diminuir os custos de classificação. O protótipo REUSE+ foi construído para exemplificar o uso do modelo e do método de classificação semi-automática, de forma a validar a proposta, destacando, por fim, as principais contribuições do trabalho.
The recent developments on components technologies have increased the number of components available to the market. These components are, however, distributed overall the world and not properly advertised to the research and development communities. Finding the appropriate components to solve a particular problem is not very straightforward and new techniques must be developed to effectively reuse components. One of the great challenges in reusing components is concerned with how to actually classify components \"properly\" in order to further retrieve them. Classifying components for effective retrieval depends on acquiring the appropriate information in classification to improve the precision and recall rates in retrieval; finding only the potentially reusable components and not missing potential solutions. At the same time, the classification and retrieval mechanisms must be easy enough to persuade developers to reuse components. This work studies the classification techniques of software components, repository and retrieval methods. Hereafter is presented a proposal of components classification model that considers not just its function, but business and quality attributes. It is proposed a semi-automatic classification mechanism of software information, allowing a cheaper classification. REUSE+ prototype was built to exemplify the use of model and method of semi-automatic classification, allowing the described proposal validation, highlighting at the end the mainly contributions of the work.

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Duncan, Patricia. "The development of a method for semi-automatic classification of built-up areas from aerial imagery." Master's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/4993.

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Includes abstract.
Includes bibliographical references.
It is essential for geospatial and mapping organisations that changes to the landscapeare regularly detected and captured, so that map databases can be updated. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa’s national mapping agency, currently relies on manual methods for digitizing features and detecting changes. These methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The objective of this research is to develop a process for semi-automatic classification of built-up areas from aerial imagery in South Africa. Built-up areas are important as they can grow and change rapidly. Since the South African landscape is varied and climatological conditions differ from one area to another, a general and robust method that can be applied across the country is needed. This project aims to find the best approach for classifying urban built-up areas from high-resolution aerial imagery by comparing various image classification methods, so that a method that is transferable and applicable in diverse South African scenes may be developed. Image classification methods were compared and it was found that pixel-based classifiers were unsatisfactory in classifying built-up areas, whereas object-based classifiers had better results. Image segmentation, the first step in an object-based classification, can considerably influence the results of the classification task. It is therefore essential that suitable image segments be generated before the segments are classified. The proposed The proposed methodology involves the use of cadastral data in the image segmentation process and texture measures in the classification of built-up areas within an object-based process. The method can be applied to diverse scenes across South Africa to find built-up areas. This is a generalised approach and can assist the CD: NGI in the process of updating their topographic database by reducing the time that operators spend on identifying and manually digitizing built-up areas.

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Martinez-Alvarez, Miguel. "Knowledge-enhanced text classification : descriptive modelling and new approaches." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/27205.

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The knowledge available to be exploited by text classification and information retrieval systems has significantly changed, both in nature and quantity, in the last years. Nowadays, there are several sources of information that can potentially improve the classification process, and systems should be able to adapt to incorporate multiple sources of available data in different formats. This fact is specially important in environments where the required information changes rapidly, and its utility may be contingent on timely implementation. For these reasons, the importance of adaptability and flexibility in information systems is rapidly growing. Current systems are usually developed for specific scenarios. As a result, significant engineering effort is needed to adapt them when new knowledge appears or there are changes in the information needs. This research investigates the usage of knowledge within text classification from two different perspectives. On one hand, the application of descriptive approaches for the seamless modelling of text classification, focusing on knowledge integration and complex data representation. The main goal is to achieve a scalable and efficient approach for rapid prototyping for Text Classification that can incorporate different sources and types of knowledge, and to minimise the gap between the mathematical definition and the modelling of a solution. On the other hand, the improvement of different steps of the classification process where knowledge exploitation has traditionally not been applied. In particular, this thesis introduces two classification sub-tasks, namely Semi-Automatic Text Classification (SATC) and Document Performance Prediction (DPP), and several methods to address them. SATC focuses on selecting the documents that are more likely to be wrongly assigned by the system to be manually classified, while automatically labelling the rest. Document performance prediction estimates the classification quality that will be achieved for a document, given a classifier. In addition, we also propose a family of evaluation metrics to measure degrees of misclassification, and an adaptive variation of k-NN.

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NUNES, RAFAEL DA SILVA. "THE CREATION OF A SEMI-AUTOMATIC CLASSIFICATION MODEL USING GEOGRAPHIC KNOWLEDGE: A CASE STUDY IN THE NORTHERN PORTION OF THE TIJUCA MASSIF - RJ." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2013. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34950@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Os processos de transformação da paisagem são resultantes da interação de elementos (bióticos e abióticos) que compõe a superfície da Terra. Baseia-se, a partir de uma perspectiva holística, no inter-relacionamento de uma série de ações e objetos que confluem para que a paisagem seja percebida como um momento sintético da confluência de inúmeras temporalidades. Desta maneira, as geotecnologias passam a se constituir como um importante aparato técnico-científico para a interpretação desta realidade ao possibilitar novas e diferentes formas do ser humano interpretar a paisagem. Um dos produtos gerados a partir desta interpretação é a classificação de uso e cobertura do solo e que se configura como um instrumento central para a análise das dinâmicas territoriais. Desta maneira, o objetivo do presente trabalho é elaboração de um modelo de classificação semi-automática baseada em conhecimento geográfico para o levantamento do padrão de uso e cobertura da paisagem a partir da utilização de imagens de satélite de alta resolução, tendo como recorte analítico uma área na porção setentrional no Maciço da Tijuca. O modelo baseado na análise de imagens baseadas em objetos, quando confrontados com a classificação visual, culminou em um valor acima de 80 por cento de correspondência tanto para imagens de 2010 e 2009, apresentando valores bastante elevados também na comparação classe a classe. A elaboração do presente modelo contribuiu diretamente para a otimização da produção dos dados elaborados contribuindo sobremaneira para a aceleração da interpretação das imagens analisadas, assim como para a minimização de erros ocasionados pela subjetividade atrelada ao próprio classificador.
The transformation processes of the landscape are results from the interaction of factors (biotic and abiotic) that makes up the Earth s surface. This interaction, from a holistic perspective, is then based on the inter-relationship of a series of actions and objects that converge so that landscape is perceived as a moment of confluence of numerous synthetic temporalities. Thus, the geotechnologies come to constitute an important technical and scientific apparatus for the interpretation of this reality by enabling new and different ways of interpreting the human landscape. One of the products that can be generated from this interpretation is the use classification and land cover and is configured as a central instrument for the analysis of territorial dynamics. Thus, the aim of this work is the development of a semi-automatic classification model based on geographic knowledge to survey the pattern of land use and cover the landscape from the use of satellite images of high resolution, with the analytical approach an area in the northern portion of the Tijuca Massif. The model built on an Object-Based Image Analysis, when confronted with the visual classification, culminated in a value above 80 percent match for 2010 and 2009, with very high values in the comparison class to class. The development of this model directly contributed to the optimization of the production of processed data contributing greatly to the acceleration of the interpretation of the images analyzed, as well as to minimize errors caused by the subjectivity linked to the classifier itself.

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Colaninno, Nicola. "Semi-automatic land cover classification and urban modelling based on morphological features : remote sensing, geographical information systems, and urban morphology : defining models of land occupation along the Mediterranean side of Spain." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/396219.

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From a global point of view, as argued by Levy (1999), the modern city has undergone radical changes in its physical form, either in terms of territorial expansion as well as in terms of interna! physical transformations. Today, approximately 75% of the European population lives in urban areas ,which makes the urban fulure of the conlinent a major cause of concern (Brazil, Cavalcanti, & Longo, 2014). lndeed, the demand for urban land, both within and around the cities, is becoming increasingly acule (European Environmenl Agency, 2006). Ouring the last decades, also Spain has been undergoing an important process of urban growth, which has implied the consumption of a large amounl of land, al hough the overall population growth rale, mostly along certain specific geographic areas, has remained at least unchanged or even, in sorne cases, il has also decreased. Such a phenomenon has been quite remarkable along the Mediterranean side. As argued by Gaja (2008), the urban development in Spain has been strongly linked to the model of economic development , which relies, since its launch in the 50's, onlhree main factors , i.e.:emigration, building, and mass tourism. Nowadays , in Spain, and mostly along the Medilerranean side, several urban areas are facing important phenomena of urban sprawl, also feared by he European Union. An accurate information about the pattern of land use/land cover, over time, is a fundamental requirement for a better understanding of the urban models. Currently, even though plenty of approaches to the image classification, through Remote Sensing (RS) techniques, have been advanced, Land Cover/Land Use classification is still an exciting challenge (Weng, 2010). Actually, the increasing development of RS and GIS technologies, during the last decades, has provided further capabiliies for measuring, analysing, understanding, modelling the "physical expressions" of urban growth phenomena, either in terms of pattern and process (Bhatta, 2012), and based on land use/land cover mapping and change delection over time. Based on such a technological approach, here we first aim to set up a suitable methodology for detecting generalized land cover classes based on an assisted automatic (or semi-aulomatic) pixel-based approach, calibrated upon Landsat Thematic Mapper (TM) mullispectral imagery, at 30 meters of spatial resolution. Beside, through the use of Geographical lnformation Syslem (GIS) we provide a spatial analysis and modelling of different urban models, from a morphological standpoint, in order to define the main pattern of land occupation al municipal scale, and along the Mediterranean side of Spain, al the year 2011. We focus on two main issues. On one hand, RS techniques have been used to set up a proper semi-automatic classification methodology, based on the use of Landsat imagery, capable of handling huge geographical areas quickly and efficiently. This process is basically aimed to detect the urban areas, at the year 2011, along the Mediterranean side of Spain, depending on the administrative division of Autonomous Communities. On the other hand, the spatial patterns of urban settlements have been analysed by using a GIS platform for quantifying a set of spatial metrics about the urban form. Hence, once get the quantification of different morphological features, including the analysis aboul either the urban profile, the urban texture, and the street network pattern, an automatic classification of different urban morphological models has been proposed, based on a stalistical approaches, namely factor and cluster analysis
Desde un punto de vista global,como sostiene Levy (1999), la ciudad moderna ha experimentado cambios radicales en su forma física, ya sea en términos de expansión territorial, así como en términos de transformaci ones internas. Hoy en día, aproximadamente el 75% de la población europea vive en zonas urbanas, lo que hace del futuro urbano delcontinente, una causa importante de preocupación (Brasil, Cavalcanti, y Longo, 2014). De hecho, la demanda de suelo urbano, dentro y alrededor de las ciudades , es cada vez más aguda (Agencia Europea de Medio Ambiente,2006). Durante las últimas décadas, también España ha experimentado un importante proceso de crecimiento urbano que ha implicado el consumo de una gran cantidad de tierra, aunque la tasa de crecimiento de la población en general, sobre todo a lo largo de ciertas áreas geográficas específicas , se ha mantenido al menos sin cambios o incluso, en algunos casos, también ha disminuido. Este fenómeno ha sido muy evidente a lo largo de la vertiente mediterránea. Como sostiene Gaja (2008), el desarrollo urbano en España se ha visto fuertemente vinculado con el modelo de desarrollo económico, que se basa, desde su lanzamiento en la década de los 50,en tres factores principales, a saber: la emigración, la construcción y el turismo de masas. Hoy en día, en España, y sobre todo a lo largo de la vertiente mediterránea, varias zonas urbanas se enfrentan a fenómenos importantes de expansión urbana, también temidos por la Unión Europea. Al respecto,un requisito fundamental para mejorar la comprensión y el estudio de los modelos urbanos es obtener en eltiempo una información precisa sobre los patrones de cubiertas y uso de suelo. Actualmente, a pesar de la existencia de numerosos métodos para la clasificación de imágenes digitales a través de técnicas de teledetección, para ext raer información sobre cobertura/uso de suelo, este enfoque sigue siendo un reto apasionante (Weng, 2010). El creciente desarrollo de las tecnologías de RS y GIS, durante las últimas décadas, ha proporcionado nuevas capacidades para medir, analizar, comprender, y modelar las "expresiones físicas" de los fenómenos de crecimiento urbano, en términos de patrones y procesos (Bhatta, 2012), y con base en el mapeo y análisis de cambios de cobertura/uso de suelo a través el tiempo. Basándose en un enfoque tecnológico, el primero objetivo es establecer una metodología adecuada para la detección de clases de cobertura de la tierra generalizadas que encuentra su fundamento en una asistido automático (o semiautomático), enfoque basado en píxeles, calibradas en Landsat Thematic Mapper (TM) imágenes multiespectrales, a 30 metros de resolución espacial. Al lado, a través del uso del Sistema de Información Geográfica (SIG), es posible proveer un análisis espacial y la modelización de diferentes modelos urbanos, desde un punto de vista morfológico, con el fin de definir el patrón principal de la ocupación del suelo a escala municipal a lo largo de la vertiente mediterránea de España, en el año 2011. En particular no enfocamos en dos cuestiones principales. Por un lado, las técnicas de RS se han utilizado para establecer una metodología de clasificación semi-automático adecuada, basada en el uso de imágenes Landsat, capaz de manejar grandes zonas geográficas de forma rápida y eficiente. Este proceso, básicamente, va dirigido a detectar las áreas urbanas, en el año 2011, a lo largo de la vertiente mediterránea de España, según la división administrativa de las Comunidades Autónomas. Por otro lado, los patrones espaciales de asentamientos urbanos han sido analizados mediante el uso de una plataforma GIS para cuantificar un conjunto de métricas espaciales sobre la forma urbana. Finalmente, una vez obtenida la cuantificación de diferentes características morfológicas, se ha proporcionado una clasificación automática de los diferentes modelos morfológicos urbanos, basada en un enfoque estadístico, es decir, análisis factorial y clúster.

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Teljstedt, Erik Christopher. "Separating Tweets from Croaks : Detecting Automated Twitter Accounts with Supervised Learning and Synthetically Constructed Training Data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192656.

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In this thesis, we have studied the problem of detecting automated Twitter accounts related to the Ukraine conflict using supervised learning. A striking problem with the collected data set is that it was initially lacking a ground truth. Traditionally, supervised learning approaches rely on manual annotation of training sets, but it incurs tedious work and becomes expensive for large and constantly changing collections. We present a novel approach to synthetically generate large amounts of labeled Twitter accounts for detection of automation using a rule-based classifier. It significantly reduces the effort and resources needed and speeds up the process of adapting classifiers to changes in the Twitter-domain. The classifiers were evaluated on a manually annotated test set of 1,000 Twitter accounts. The results show that rule-based classifier by itself achieves a precision of 94.6% and a recall of 52.9%. Furthermore, the results showed that classifiers based on supervised learning could learn from the synthetically generated labels. At best, the these machine learning based classifiers achieved a slightly lower precision of 94.1% compared to the rule-based classifier, but at a significantly better recall of 93.9%
Detta exjobb har undersökt problemet att detektera automatiserade Twitter-konton relaterade till Ukraina-konflikten genom att använda övervakade maskininlärningsmetoder. Ett slående problem med den insamlade datamängden var avsaknaden av träningsexempel. I övervakad maskininlärning brukar man traditionellt manuellt märka upp en träningsmängd. Detta medför dock långtråkigt arbete samt att det blir dyrt förstora och ständigt föränderliga datamängder. Vi presenterar en ny metod för att syntetiskt generera uppmärkt Twitter-data (klassifieringsetiketter) för detektering av automatiserade konton med en regel-baseradeklassificerare. Metoden medför en signifikant minskning av resurser och anstränging samt snabbar upp processen att anpassa klassificerare till förändringar i Twitter-domänen. En utvärdering av klassificerare utfördes på en manuellt uppmärkt testmängd bestående av 1,000 Twitter-konton. Resultaten visar att den regelbaserade klassificeraren på egen hand uppnår en precision på 94.6% och en recall på 52.9%. Vidare påvisar resultaten att klassificerare baserat på övervakad maskininlärning kunde lära sig från syntetiskt uppmärkt data. I bästa fall uppnår dessa maskininlärningsbaserade klassificerare en något lägre precision på 94.1%, jämfört med den regelbaserade klassificeraren, men med en betydligt bättre recall på 93.9%.

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Book chapters on the topic "Semi-automatic classification"

Corella, Miguel Ángel, and Pablo Castells. "Semi-automatic Semantic-Based Web Service Classification." In Business Process Management Workshops, 459–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11837862_43.

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Martinez-Alvarez, Miguel, Sirvan Yahyaei, and Thomas Roelleke. "Semi-automatic Document Classification: Exploiting Document Difficulty." In Lecture Notes in Computer Science, 468–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28997-2_43.

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Martinez-Alvarez, Miguel, Alejandro Bellogin, and Thomas Roelleke. "Document Difficulty Framework for Semi-automatic Text Classification." In Data Warehousing and Knowledge Discovery, 110–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40131-2_10.

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Mikelić Preradović, Nives, and Damir Boras. "Semi-automatic Verb Valence Frame Assignment through VerbNet Classification." In Text, Speech, and Dialogue, 492–500. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40585-3_62.

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Elmoasry, Ahmed, Mohamed Sadek Maswadah, and Lars Linsen. "Semi-Automatic Rough Classification of Multichannel Medical Imaging Data." In Mathematics and Visualization, 71–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-21608-4_5.

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Böck, Ronald, Kerstin Limbrecht-Ecklundt, Ingo Siegert, Steffen Walter, and Andreas Wendemuth. "Audio-Based Pre-classification for Semi-automatic Facial Expression Coding." In Human-Computer Interaction. Towards Intelligent and Implicit Interaction, 301–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39342-6_33.

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Wu, Jian, Chen Ye, Victor S. Sheng, Yufeng Yao, Pengpeng Zhao, and Zhiming Cui. "Semi-automatic Labeling with Active Learning for Multi-label Image Classification." In Lecture Notes in Computer Science, 473–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24075-6_46.

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Blahuta, Jiří, Tomáš Soukup, Petr Čermák, David Novák, and Michal Večerek. "Semi-automatic Ultrasound Medical Image Recognition for Diseases Classification in Neurology." In Advances in Intelligent Analysis of Medical Data and Decision Support Systems, 125–33. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00029-9_11.

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ten Haaf, Fenna, Christopher Claassen, Ruben Eschauzier, Joanne Tjan, Daniël Buijs, Flavius Frasincar, and Kim Schouten. "WEB-SOBA: Word Embeddings-Based Semi-automatic Ontology Building for Aspect-Based Sentiment Classification." In The Semantic Web, 340–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77385-4_20.

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Marcomini, Karem D., Eduardo F. C. Fleury, Homero Schiabel, and Robert M. Nishikawa. "Proposal of Semi-automatic Classification of Breast Lesions for Strain Sonoelastography Using a Dedicated CAD System." In Breast Imaging, 454–60. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41546-8_57.

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Conference papers on the topic "Semi-automatic classification"

Zhang, Tong. "Semi-automatic approach for music classification." In ITCom 2003, edited by John R. Smith, Sethuraman Panchanathan, and Tong Zhang. SPIE, 2003. http://dx.doi.org/10.1117/12.511787.

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Bakhti, Khadidja, Zhendong Niu, and Ally S. Nyamawe. "Semi-Automatic Annotation for Citation Function Classification." In 2018 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO). IEEE, 2018. http://dx.doi.org/10.1109/iccairo.2018.00016.

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Yang, Jie, and Xianzhong Zhou. "Semi-automatic Algorithem Based on Web Service Classification." In Information Science for Industry 2014. Science & Engineering Research Support soCiety, 2014. http://dx.doi.org/10.14257/astl.2014.53.21.

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Betz, Simon, Jana Voße, Sina Zarrieß, and Petra Wagner. "Increasing Recall of Lengthening Detection via Semi-Automatic Classification." In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1528.

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Nunes, Bernardo Pereira, Giseli Rabello Lopes, and Marco Antonio Casanova. "Automatic Classification and Taxonomy Generation for Semi-structured Data." In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM). IEEE, 2015. http://dx.doi.org/10.1109/cit/iucc/dasc/picom.2015.30.

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Xu, Ge, and Houfeng Wang. "Semi-supervised Chinese contextual polarity classification with automatic feature selection." In 2011 Seventh International Conference on Natural Computation (ICNC). IEEE, 2011. http://dx.doi.org/10.1109/icnc.2011.6022245.

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Takagi, Jun, Yasunori Ohishi, Akisato Kimura, Masashi Sugiyama, Makoto Yamada, and Hirokazu Kameoka. "Automatic audio tag classification via semi-supervised canonical density estimation." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946925.

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Creusen, I. M., L. Hazelhoff, and P. H. N. de With. "A semi-automatic traffic sign detection, classification, and positioning system." In IS&T/SPIE Electronic Imaging, edited by Amir Said, Onur G. Guleryuz, and Robert L. Stevenson. SPIE, 2012. http://dx.doi.org/10.1117/12.908552.

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Benato, Bárbara C., Alexandru C. Telea, and Alexandre X. Falcão. "Semi-Automatic Data Annotation guided by Feature Space Projection." In Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sibgrapi.est.2020.12976.

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Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analysis of their feature space projection (with no further sample supervision). We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation. We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities, a large and diverse dataset that makes classification very hard. We evaluate two approaches for semi-supervised learning from the latent and projection spaces, to choose the one that best reduces user annotation effort and also increases classification accuracy on unseen data. Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning.

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Drumetz, L., M. Dalla Mura, S. Meulenyzer, S. Lombard, and J. Chanussot. "Semi-automatic classification of cementitious materials using scanning electron microscope images." In The International Conference on Quality Control by Artificial Vision 2015, edited by Fabrice Meriaudeau and Olivier Aubreton. SPIE, 2015. http://dx.doi.org/10.1117/12.2182762.

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