23,174 research outputs found
A hybrid deep learning approach for texture analysis
Texture classification is a problem that has various applications such as remote sensing and forest species recognition. Solutions tend to be custom fit to the dataset used but fails to generalize. The Convolutional Neural Network (CNN) in combination with Support Vector Machine (SVM) form a robust selection between powerful invariant feature extractor and accurate classifier. The fusion of classifiers shows the stability of classification among different datasets and slight improvement compared to state of the art methods. The classifiers are fused using confusion matrix after independent training of each using the same training set, then put to test. Statistical information about each classifier is fed to a confusion matrix that generates two confidence measures used in building two binary classifiers. The binary classifier is allowed to activate or deactivate a classifier during testing time based on a confidence measure obtained from the confusion matrix. The method obtained results approaching state of the art with a difference less than 1% in classification success rates. Moreover, the method was able to maintain this success rate among different datasets while other methods had failed to obtain similar stability. Two datasets had been used in this research Brodatz and Kylberg where the results came 98.17% and 99.70%. In comparison to conventional methods in the literature, it came as 98.9% and 99.64% respectively
Dynamic Bayesian Combination of Multiple Imperfect Classifiers
Classifier combination methods need to make best use of the outputs of
multiple, imperfect classifiers to enable higher accuracy classifications. In
many situations, such as when human decisions need to be combined, the base
decisions can vary enormously in reliability. A Bayesian approach to such
uncertain combination allows us to infer the differences in performance between
individuals and to incorporate any available prior knowledge about their
abilities when training data is sparse. In this paper we explore Bayesian
classifier combination, using the computationally efficient framework of
variational Bayesian inference. We apply the approach to real data from a large
citizen science project, Galaxy Zoo Supernovae, and show that our method far
outperforms other established approaches to imperfect decision combination. We
go on to analyse the putative community structure of the decision makers, based
on their inferred decision making strategies, and show that natural groupings
are formed. Finally we present a dynamic Bayesian classifier combination
approach and investigate the changes in base classifier performance over time.Comment: 35 pages, 12 figure
Evaluation of Performance Measures for Classifiers Comparison
The selection of the best classification algorithm for a given dataset is a
very widespread problem, occuring each time one has to choose a classifier to
solve a real-world problem. It is also a complex task with many important
methodological decisions to make. Among those, one of the most crucial is the
choice of an appropriate measure in order to properly assess the classification
performance and rank the algorithms. In this article, we focus on this specific
task. We present the most popular measures and compare their behavior through
discrimination plots. We then discuss their properties from a more theoretical
perspective. It turns out several of them are equivalent for classifiers
comparison purposes. Futhermore. they can also lead to interpretation problems.
Among the numerous measures proposed over the years, it appears that the
classical overall success rate and marginal rates are the more suitable for
classifier comparison task
Features for the classification and clustering of music in symbolic format
Tese de mestrado, Engenharia InformĂĄtica, Universidade de Lisboa, Faculdade de CiĂȘncias, 2008Este documento descreve o trabalho realizado no Ăąmbito da disciplina de Projecto em Engenharia InformĂĄtica do Mestrado em Engenharia InformĂĄtica da Faculdade de CiĂȘncias da Universidade de Lisboa. Recuperação de Informação Musical Ă©, hoje em dia, um ramo altamente activo de investigação e desenvolvimento na ĂĄrea de ciĂȘncia da computação, e incide em diversos tĂłpicos, incluindo a classificação musical por gĂ©neros. O trabalho apresentado centra-se na Classificação de Pistas e de GĂ©neros de mĂșsica armazenada usando o formato MIDI. Para resolver o problema da classificação de pistas MIDI, extraimos um conjunto de descritores que sĂŁo usados para treinar um classificador implementado atravĂ©s de uma tĂ©cnica de MĂĄquinas de Aprendizagem, Redes Neuronais, com base nas notas, e duraçÔes destas, que descrevem cada faixa. As faixas sĂŁo classificadas em seis categorias: Melody (Melodia), Harmony (Harmonia), Bass (Baixo) e Drums (Bateria). Para caracterizar o conteĂșdo musical de cada faixa, um vector de descritores numĂ©rico, normalmente conhecido como âshallow structure descriptionâ, Ă© extraĂdo. Em seguida, eles sĂŁo utilizados no classificador â Neural Network â que foi implementado no ambiente Matlab. Na Classificação por GĂ©neros, duas propostas foram usadas: Modelação de Linguagem, na qual uma matriz de transição de probabilidades Ă© criada para cada tipo de pista midi (Melodia, Harmonia, Baixo e Bateria) e tambĂ©m para cada gĂ©nero; e Redes Neuronais, em que um vector de descritores numĂ©ricos Ă© extraĂdo de cada pista, e Ă© processado num Classificador baseado numa Rede Neuronal. Seis ColectĂąneas de Musica no formato Midi, de seis gĂ©neros diferentes, Blues, Country, Jazz, Metal, Punk e Rock, foram formadas para efectuar as experiĂȘncias. Estes gĂ©neros foram escolhidos por partilharem os mesmos instrumentos, na sua maioria, como por exemplo, baixo, bateria, piano ou guitarra. Estes gĂ©neros tambĂ©m partilham algumas caracterĂsticas entre si, para que a classificação nĂŁo seja trivial, e para que a robustez dos classificadores seja testada. As experiĂȘncias de Classificação de Pistas Midi, nas quais foram testados, numa primeira abordagem, todos os descritores, e numa segunda abordagem, os melhores descritores, mostrando que o uso de todos os descritores Ă© uma abordagem errada, uma vez que existem descritores que confundem o classificador. Provou-se que a melhor maneira, neste contexto, de se classificar estas faixas MIDI Ă© utilizar descritores cuidadosamente seleccionados. As experiĂȘncias de Classificação por GĂ©neros, mostraram que os Classificadores por Instrumentos (Single-Instrument) obtiveram os melhores resultados. Quatro gĂ©neros, Jazz, Country, Metal e Punk, obtiveram resultados de classificação com sucesso acima dos 80%
O trabalho futuro inclui: algoritmos genĂ©ticos para a selecção de melhores descritores; estruturar pistas e musicas; fundir todos os classificadores desenvolvidos num Ășnico classificador.This document describes the work carried out under the discipline of Computing Engineering Project of the Computer Engineering Master, Sciences Faculty of the Lisbon University. Music Information Retrieval is, nowadays, a highly active branch of research and development in the computer science field, and focuses several topics, including music genre classification. The work presented in this paper focus on Track and Genre Classification of music stored using MIDI format, To address the problem of MIDI track classification, we extract a set of descriptors that are used to train a classifier implemented by a Neural Network, based on the pitch levels and durations that describe each track. Tracks are classified into four classes: Melody, Harmony, Bass and Drums. In order to characterize the musical content from each track, a vector of numeric descriptors, normally known as shallow structure description, is extracted. Then they are used as inputs for the classifier which was implemented in the Matlab environment. In the Genre Classification task, two approaches are used: Language Modeling, in which a transition probabilities matrix is created for each type of track (Melody, Harmony, Bass and Drums) and also for each genre; and an approach based on Neural Networks, where a vector of numeric descriptors is extracted from each track (Melody, Harmony, Bass and Drums) and fed to a Neural Network Classifier. Six MIDI Music Corpora were assembled for the experiments, from six different genres, Blues, Country, Jazz, Metal, Punk and Rock. These genres were selected because all of them have the same base instruments, such as bass, drums, piano or guitar. Also, the genres chosen share some characteristics between them, so that the classification isnât trivial, and tests the classifiers robustness. Track Classification experiments using all descriptors and best descriptors were made, showing that using all descriptors is a wrong approach, as there are descriptors which confuse the classifier. Using carefully selected descriptors proved to be the best way to classify these MIDI tracks. Genre Classification experiments showed that the Single-Instrument Classifiers achieved the best results. Four genres achieved higher than 80% success rates: Jazz, Country, Metal and Punk. Future work includes: genetic algorithms; structurize tracks and songs; merge all presented classifiers into one full Automatic Genre Classification System
An Overview of Classifier Fusion Methods
A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing âpudding
of diversitiesâ is also provided
Examining Variations of Prominent Features in Genre Classification.
This paper investigates the correlation between features of three types (visual, stylistic and topical types) and genre classes. The majority of previous studies in automated genre classification have created models based on an amalgamated representation of a document using a combination of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. In this paper we use classifiers independently modeled on three groups of features to examine six genre classes to show that the strongest features for making one classification is not necessarily the best features for carrying out another classification.
Accuracy Measures for the Comparison of Classifiers
The selection of the best classification algorithm for a given dataset is a
very widespread problem. It is also a complex one, in the sense it requires to
make several important methodological choices. Among them, in this work we
focus on the measure used to assess the classification performance and rank the
algorithms. We present the most popular measures and discuss their properties.
Despite the numerous measures proposed over the years, many of them turn out to
be equivalent in this specific case, to have interpretation problems, or to be
unsuitable for our purpose. Consequently, classic overall success rate or
marginal rates should be preferred for this specific task.Comment: The 5th International Conference on Information Technology, amman :
Jordanie (2011
An Overview of Classifier Fusion Methods
A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing âpudding
of diversitiesâ is also provided
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