Localised contourlet features in vehicle make and model recognition

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance

Two dimensional statistical linear discriminant analysis for real-time robust vehicle type recognition

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm’s robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach

Estudio sobre la cantidad mínima de muestras de entrenamiento para la clasificación de modelos vehiculares

La clasificación de objetos es uno de los campos de estudios más importantes de los últimos años y está asociado a la similitud de características entre los objetos y al continuo crecimiento de los conjuntos de datos de entrenamiento. En base a ello, aumentar el número de muestras de entrenamiento mejora el rendimiento de los clasificadores. Sin embargo, no hay estudios que determinen un estimado de cuántas muestras de entrenamiento son necesarias para generar clasificadores robustos. En esta investigación se intenta responder esta pregunta, enfocando el problema en la clasificación por marca y modelo vehicular. Para ello, se creó un conjunto de datos compuesto por 32 modelos vehiculares diferentes y se utilizó la red VGG16 para la tarea de extracción de características. Asimismo, se utilizaron los algoritmos de clasificación Máquinas de Vector Soporte (SVM), Bosques Aleatorios (RF), Árboles de Decisión (DT) y Naive Bayes (NB). Se realizaron conjunto de entrenamientos en los que se variaron el número de muestras de entrenamiento y el número de categorías a clasificar por cada algoritmo. En estos experimentos, el algoritmo SVM fue el de mayor precisión con un 96.82% para el caso de 32 modelos vehiculares diferentes. Finalmente, se determinó que a medida que se aumenta el número de modelos vehiculares a clasificar, es necesario aumentar las muestras de entrenamiento para estabilizar la precisión, y que el número mínimo de muestras para este comportamiento es de 400 muestras para el escenario de 2 categorías y de 700 muestras para el resto de los escenarios con más categorías.Object classification is one of the most important fields of study in recent times and it is associated with the similarity between objects and the continuous growth of training data sets. Based on this, increasing the number of training samples improves the performance of the classifiers. However, there are no studies that determine an estimate of how many training samples are necessary to develop solid classifiers. This research attempts to answer this question, focusing the problem on vehicle make and model recognition (VMMR). To do this, a data set composed of 32 different vehicle models was created and the VGG16 network was used for the feature extraction task. Likewise, the Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT) and Naive Bayes (NB) classification algorithms were used. A set of experiments were carried out in which the number of training samples and the number of categories to be classified by each algorithm were varied. In these experiments, the SVM algorithm was the most accurate with 96.82% for the case of 32 different vehicle models. Finally, it was determined that as the number of vehicle models to be classified is increased, it is necessary to increase the training samples, to stabilize the precision, and that the minimum number of training samples for this stabilization is 400 samples for the scenario of 2 categories and 700 samples for the rest of the scenarios with more categories

Principal Component Analysis

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Structured representation learning from complex data

This thesis advances several theoretical and practical aspects of the recently introduced restricted Boltzmann machine - a powerful probabilistic and generative framework for modelling data and learning representations. The contributions of this study represent a systematic and common theme in learning structured representations from complex data