Instead of solving complex pattern recognition problems using a single complicated classifier, it is often beneficial to leverage our prior knowledge and decompose the problem into parts. These may be tackled using specific feature subsets and simpler classifiers resulting in a hierarchical system. In this paper, we propose an efficient and scalable approach for cost-sensitive optimization of a general hierarchical classifier using ROC analysis. This allows the designer to view the hierarchy of trained classifiers as a system, and tune it according to the application needs.

Receiver Operating Characteristic (ROC) analysis enables fine-tuning of a trained classifier to a desired performance trade-off situation. ROC estimated from a finite test set is, however, insufficient for the sake of classifier comparison as it neglects performance variances. This research presents a practical algorithm for variance estimation at individual operating points of ROC curves or surfaces. It generalizes the threshold averaging of Fawcett et.al. to arbitrary operating point definition including the weighting-based formulation used in multi-class ROC analysis. The statistical test comparing performance differences between operating points of the same curve is illustrated for two-class and multi-class ROC.

Deriving an informative data representation is an important prerequisite when designing road-sign classifiers. A frequently used strategy for road-sign classification is based on the normalized cross correlation similarity to class prototypes followed by the nearest neighbor classifier. Because of the global nature of the cross correlation similarity, this method suffers from presence of uninformative pixels (caused, e.g., by occlusions) and is computationally demanding. In this paper, a novel concept of a trainable similarity measure is introduced, which alleviates these shortcomings. The similarity is based on individual matches in a set of local image regions. The set of regions that are relevant for a particular similarity assessment is refined by the training process. It is illustrated on a set of experiments with road-sign-classification problems that the trainable similarity yields high-performance data representations and classifiers. Apart from a multiclass classification accuracy, nonsign rejection capability and computational demands in execution are also discussed. It appears that the trainable similarity representation alleviates some difficulties of other algorithms that are currently used in road-sign classification.

A flexible description of images is offered by a cloud of points in a feature space. In the context of image retrieval such clouds can be represented in a number of ways. Two approaches are here considered. The first approach is based on the assumption of a normal distribution, hence homogeneous clouds, while the second one focuses on the boundary description, which is more suitable for multimodal clouds. The images are then compared either by using the Mahalanobis distance or by the support vector data description (SVDD), respectively. The paper investigates some possibilities of combining the image clouds based on the idea that responses of several cloud descriptions may convey a pattern, specific for semantically similar images. A ranking of image dissimilarities is used as a comparison for two image databases targeting image classification and retrieval problems. We show that combining of the SVDD descriptions improves the retrieval performance with respect to ranking, on the contrary to the Mahalanobis case. Surprisingly, it turns out that the ranking of the Mahalanobis distances works well also for inhomogeneous images.