Car make and model recognition under limited lighting conditions at night

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyCar make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when licence plate numbers cannot be identified or fake number plates are used. CMMR can also be used when automatic identification of a certain model of a vehicle by camera is required. The majority of existing CMMR methods are designed to be used only in daytime when most car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. This work identifies car make and model at night by using available rear view features. A binary classifier ensemble is presented, designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and licence plates from the rear view are extracted and used in the recognition process. The majority vote of individual classifiers, support vector machine, decision tree, and k-nearest neighbours is applied to verify a target model in the classification process. The experiments on 100 car makes and models captured under limited lighting conditions at night against about 400 other car models show average high classification accuracy about 93%. The classification accuracy of the presented technique, 93%, is a bit lower than the daytime technique, as reported at 98 % tested on 21 CMMs (Zhang, 2013). However, with the limitation of car appearances at night, the classification accuracy of the car appearances gained from the technique used in this study is satisfied

Predicción y selección de características, mediante análisis local de la fiabilidad, para el mercado de valores y su extensión a problemas de clasificación y regresión

Esta tesis se encuadra dentro del ámbito del Aprendizaje Automático, un área de la Inteligencia Artificial (IA). A lo largo de la misma, se han diseñado y validado experimentalmente, nuevas técnicas de selección de atributos y de clasificación. La motivación para el desarrollo de dichas técnicas, se basa en el deseo de implementar herramientas adecuadas para tratar problemas de selección de atributos y de clasificación en un dominio de especial dificultad: el mercado de valores. Se ha partido de la hipótesis de que los factores que dificultan la clasificación correcta de los datos son, a menudo, una ratio desfavorable entre información y ruido, una alta dimensionalidad, escasez de patrones y desbalanceo del número de patrones de cada clase. Una vez identificados dichos factores, se han diseñado técnicas robustas frente a estos, concretamente un algoritmo de selección de atributos (con diferentes variantes) y un algoritmo de clasificación. Estas técnicas se han validado sobre un exhaustivo conjunto de problemas generados artificialmente y en problemas reales del mercado de valores. Por último, se ha explorado la posibilidad de utilizar las nuevas técnicas de selección de atributos propuestas en problemas convencionales. Para ello, se han validado sobre un conjunto de dominios reales de uso común en Aprendizaje Automático, tanto para clasificación como para regresión.This thesis belongs to Machine Learning, an area of Artificial Intelligence (AI). During its development, new techniques of attribute selection and classification have been designed and validated empirically. The motivation for the development of these techniques is based on the desire to implement adequate tools to deal with feature selection and classification problems in an area of particular difficulty: the stock market. Based on the hypothesis that the factors which make data classification difficult are, frequently, a low ratio between information and noise; high dimensionality, small training samples, and class imbalance. Once these factors have been identified, robust techniques to deal with them were designed, specifically a feature selection algorithm (with different variants) and a classification algorithm. These techniques have been validated over exhaustive synthetic data sets and stock market problems. Finally, the possibility of using the new feature selection techniques were explored in conventional problems. To this end, they were validated using a data set of actual domains, both for classification and regression.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Pedro Isasi Viñuela.- Secretario: David Camacho Fernández.- Vocal: Sonia Schulenbur

Projection-based measure for efficient feature selection

The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. Depending on the method to apply: starting point, search organization, evaluation strategy, and the stopping criterion, there is an added cost to the classification algorithm that we are going to use, that normally will be compensated, in greater or smaller extent, by the attribute reduction in the classification model. The method proposed in this work utilizes a measure based on projections to guide the selection of the attributes. The algorithm (SOAP: Selection of Attributes by Projection) has some interesting characteristics: lower computational cost (O(mn log n) m attributes and n examples in the data set) with respect to other typical algorithms due to the absence of distance and statistical calculations; its applicability to any labelled data set, that is to say, it can contain continuous and discrete variables, with no need for transformation. The performance of SOAP is analysed in two ways: percentage of reduction and classification. SOAP has been compared to CFS [4] and ReliefF [8]. The results are generated by C4.5 before and after the application of the algorithms