Alceu S. Britto

Dynamic selection of classifiers-A comprehensive review

This work presents a literature review of multiple classifier systems based on the dynamic selection of classifiers. First, it briefly reviews some basic concepts and definitions related to such a classification approach and then it presents the state of the art organized according to a proposed taxonomy. In addition, a two-step analysis is applied to the results of the main methods reported in the literature, considering different classification problems. The first step is based on statistical analyses of the significance of these results. The idea is to figure out the problems for which a significant contribution can be observed in terms of classification performance by using a dynamic selection approach. The second step, based on data complexity measures, is used to investigate whether or not a relation exists between the possible performance contribution and the complexity of the classification problem. From this comprehensive study, we observed that, for some classification problems, the performance contribution of the dynamic selection approach is statistically significant when compared to that of a single-based classifier. In addition, we found evidence of a relation between the observed performance contribution and the complexity of the classification problem. These observations allow us to suggest, from the classification problem complexity, that further work should be done to predict whether or not to use a dynamic selection approach.

2D Principal Component Analysis for Face and Facial-Expression Recognition

Although it shows enormous potential as a feature extractor, 2D principal component analysis produces numerous coefficients. Using a feature-selection algorithm based on a multiobjective genetic algorithm to analyze and discard irrelevant coefficients offers a solution that considerably reduces the number of coefficients, while also improving recognition rates.

PKLot-A robust dataset for parking lot classification

Outdoor parking lot vacancy detection systems have attracted a great deal of attention in the last decade due the large number of practical applications. However, a common problem that researchers in this field very often face is the lack of a representative dataset to perform their experiments. To mitigate this difficulty, in this paper we introduce a new parking lot dataset composed of 695,899 images captured from two parking lots with three different camera views. The acquisition protocol allows obtaining static images showing illumination variance related to sunny, overcast and rainy days. We believe that researchers will find this dataset a very useful tool since it allows future benchmarking and evaluation. The dataset is currently available for research purposes upon request. To gain a better insight into this dataset we have evaluated two textural descriptors, Local Binary Patterns and Local Phase Quantization, with a Support Vector Machine classifier to detect parking lot vacancy. In the experiments where the same view was used for both training and testing, we have reached outstanding recognition rates, greater than 99%. The main challenge, though, lies in building a general classifier that is able to detect parking spaces from the parking lots that were not used for training. In this sense, the best result achieved by the texture-based classifier was about 89%. The observed drop in terms of performance shows that additional investigation is necessary to create classification schemes less dependent on the training set. Other researchers can use these results as a baseline performance when testing their own algorithms on this dataset.

Visual and acoustic identification of bird species

This paper presents a novel approach for bird species identification that relies on both visual features extracted from unconstrained bird images and acoustic features extracted from bird vocalizations. The Scale Invariant Feature Transform (SIFT) detects local features in bird images, which are then used to train a support vector machine classifier. The instances that are not classified with a certain degree of certainty are then rejected and reclassified using Mel-frequency cepstral coefficients (MFCCs) extracted from the bird songs if available. Experiments conducted on a dataset of 50 bird species that comprise images from the CUB200-2011 and audio samples from Xeno-Canto have shown that improvements between 1.2 and 15.7 percentage points are achieved when using an acoustic classifier to re-process the instances rejected by the visual classifier, depending on the rejection level.

An HMM-Based Gesture Recognition Method Trained on Few Samples

This paper addresses the problem of recognizing gestures which are captured using the Kinect sensor in a educational game devoted to the deaf community. Different strategies are evaluated to deal with the problem of having few samples for training. We have experimented a Leave One Out Training and Testing (LOOT) strategy and an HMM-based ensemble of classifiers. A dataset containing 181 videos of gestures related to nine signs commonly used in educational games is introduced, which is available for research purposes. The experimental results have shown that the proposed ensemble-based method is a promising strategy to deal with problems where few training samples are available.

A framework for dynamic classifier selection oriented by the classification problem difficulty

Abstract This paper describes a framework for Dynamic Classifier Selection (DCS) whose novelty resides in its use of features that address the difficulty posed by the classification problem in terms of orienting both pool generation and classifier selection. The classification difficulty is described by meta-features estimated from problem data using complexity measures. Firstly, these features are used to drive the classifier pool generation expecting a better coverage of the problem space, and then, a dynamic classifier selection based on similar features estimates the ability of the classifiers to deal with the test instance. The rationale here is to dynamically select a classifier trained on a subproblem (training subset) having a similar level of difficulty as that observed in the neighborhood of the test instance defined in a validation set. A robust experimental protocol based on 30 datasets, and considering 20 replications, has confirmed that a better understanding of the classification problem difficulty may positively impact the performance of a DCS. For the pool generation method, it was observed that in 126 of 180 experiments (70.0%) adopting the proposed pool generator allowed an improvement of the accuracy of the evaluated DCS methods. In addition, the main results from the proposed framework, in which pool generation and classifier selection are both based on problem difficulty features, are very promising. In 165 of 180 experiments (91.6%), it was also observed that the proposed DCS framework based on the problem difficulty achieved a better classification accuracy when compared to 6 well known DCS methods in the literature.

Adapting dynamic classifier selection for concept drift

Abstract One popular approach employed to tackle classification problems in a static environment consists in using a Dynamic Classifier Selection (DCS)-based method to select a custom classifier/ensemble for each test instance according to its neighborhood in a validation set, where the selection can be considered region-dependent. This idea can be extended to concept drift scenarios, where the distribution or the a posteriori probabilities may change over time. Nevertheless, in these scenarios, the classifier selection becomes not only region but also time-dependent. By adding a time dependency, in this work, we hypothesize that any DCS-based approach can be used to handle concept drift problems. Since some regions may not be affected by a concept drift, we introduce the idea of concept diversity, which shows that a pool containing classifiers trained under different concepts may be beneficial when dealing with concept drift problems through a DCS approach. The impacts of pruning mechanisms are discussed and seven well-known DCS methods are evaluated in the proposed framework, using a robust experimental protocol based on 12 common concept drift problems with different properties, and the PKLot dataset considering an experimental protocol specially designed in this work to test concept drift methods. The experimental results have shown that the DCS approach comes out ahead in terms of stability, i.e., it performs well in most cases requiring almost no parameter tuning.

Towards a SignWriting recognition system

SignWriting is a writing system for sign languages. It is based on visual symbols to represent the hand shapes, movements and facial expressions, among other elements. It has been adopted by more than 40 countries, but to ensure the social integration of the deaf community, writing systems based on sign languages should be properly incorporated into the Information Technology. This article reports our first efforts toward the implementation of an automatic reading system for SignWiring. This would allow converting the SignWriting script into text so that one can store, retrieve, and index information in an efficient way. In order to make this work possible, we have been collecting a database of hand configurations, which at the present moment sums up to 7,994 images divided into 103 classes of symbols. To classify such symbols, we have performed a comprehensive set of experiments using different features, classifiers, and combination strategies. The best result, 94.4% of recognition rate, was achieved by a Convolutional Neural Network.

Inspeção Automática de Defeitos em Madeiras de Pinus usando Visão Computacional

Resumo: Este artigo apresenta um metodo completo para a deteccao de defeitos em tabuas de madeira de Pinus atraves de tecnicas de Visao Computacional. As imagens dos lados da tabua de madeira sao adquiridas com câmeras tipo line scan e processadas para extracao de caracteristicas baseadas na informacao cor e em propriedades de textura: suavidade, aspereza e regularidade. Um subconjunto destas caracteristicas, extraido a partir de imagens em niveis de cinza e selecionado com base em algoritmos geneticos multi-objetivos e proposto como alternativa para reducao de custos no processo de aquisicao de imagens. Dois paradigmas de aprendizagem de maquina diferentes foram utilizados: redes neurais e maquinas de vetor de suporte. Resultados experimentais demonstram que o conjunto de caracteristicas selecionado a partir de imagens em niveis de cinza atingiu desempenho competitivo para o problema de deteccao de defeitos em madeira, quando comparado com conjunto de caracteristicas que depende de sensor de aquisicao de maior custo (line scan colorida) para extracao de caracteristicas baseadas na informacao cor.