A partially unsupervised approach to the classification of multitemporal remote-sensing images is presented. Such an approach allows the automatic classification of a remote-sensing image for which training data are not available, drawing on the information derived from an image acquired in the same area at a previous time. In particular, the proposed technique is based on a cascade-classifier ...

In this paper we propose an approach for ensemble construction based on the use of supervised projections, both linear and non-linear, to achieve both accuracy and diversity of individual classifiers. The proposed approach uses the philosophy of boosting, putting more effort on difficult instances, but instead of learning the classifier on a biased distribution of the training set, it uses misc...

In this paper, we report an experimental comparison between two widely used combination methods, i.e. sum and product rules, in order to determine the relationship between their performance and classifier diversity. We focus on the behaviour of the considered combination rules for ensembles of classifiers with different performance and level of correlation. To this end, a simulation method is p...

This paper describes a traffic sign detection framework for greyscale images. The system is a heterogeneous cascade classifier formed by a rectangle features cascade followed by specific filters for each shape. We define two types of filters: The first one is based on local changes of the gradient direction and the second one is based on the idea of radial symmetry and gives us the centre of ci...

Broad classes of statistical classification algorithms have been developed and applied successfully to a wide range of real world domains. In general, ensuring that the particular classification algorithm matches the properties of the data is crucial in providing results that meet the needs of the particular application domain. One way in which the impact of this algorithm/application match can...

The combination of multiple classifiers to generate a single classifier has been shown to be very useful in practice. Similarly, several efforts have shown that cluster ensembles can improve the quality of results as compared to a single clustering solution. These observations suggest that ensembles containing both classifiers and clusterers are potentially useful as well. Specifically, cluster...

Ensemble learning algorithms combine the results of several classifiers to yield an aggregate classification. We present a normative evaluation of combination methods, applying and extending existing axiomatizations from Social Choice theory and Statistics. For the case of multiple classes, we show that several seemingly innocuous and desirable properties are mutually satisfied only by a dictat...

The task of classification with imbalanced datasets have attracted quite interest from researchers in the last years. The reason behind this fact is that many applications and real problems present this feature, causing standard learning algorithms not reaching the expected performance. Accordingly, many approaches have been designed to address this problem from different perspectives, i.e., da...

I hereby declare that the work submitted for assessment is original and my own work, except where acknowledged in the submission. Abstract This report presents the measurement of vehicular speed using a smartphone camera. The speed measurement is accomplished by detecting the position of the vehicle on a camera frame using OpenCV's library LBP cascade classifier, the displacement of the detecte...

Recent work in Bayesian classifiers has shown that a better and more flexible representation of domain knowledge results in better classification accuracy. In previous work [1], we have introduced a new type of Bayesian classifier called Case-Based Bayesian Network (CBBN) classifiers. We have shown that CBBNs can capture finer levels of semantics than possible in traditional Bayesian Networks (...