Data fusion methods can take advantage of the concepts of diversity and redundancy to improve system performance. Diversity can be used to improve system performance through the incorporation of different information. Similarly, redundancy can achieve the same goals through the re-use of data. These concepts have been thoroughly applied on pattern recognition problems. The basic idea is that if...

Complex target recognition tasks rarely succeed through the application of just one classification scheme. Using the combination/fusion of different classifiers based on Inverse Synthetic Aperture Radar (ISAR) images usually explore complementary information. Thus, the each individual classifier results will be combined in order to improve the global recognition rate. Automatic target recogniti...

There are two main paradigms in combining different classification algorithms: Classifier Selection and Classifier Fusion. The first one selects a single model for classifying a new instance, while the latter combines the decisions of all models. The work presented in this paper stands in between these two paradigms aiming tackle the disadvantages and benefit from the advantages of both. In par...

A perception system for pedestrian detection in urban scenarios using information from a LIDAR and a single camera is presented. Two sensor fusion architectures are described, a centralized and a decentralized one. In the former, the fusion process occurs at the feature level, i.e., features from LIDAR and vision spaces are combined in a single vector for posterior classification using a single...

This paper investigates the use of the ρ-correlation as a measure for classifier diversity to aid in the choice of classifiers for a fusion ensemble. Specifically, we define a measure that captures the correlation for n classifiers for binary output as well as for classifier with continuous output. We then suggest the use of the ρ-correlation in classifier selection where classifiers are picked...

A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building approaches. These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. This paper prese...

This paper propose two decision fusion-based multitemporal classifiers, namely, the jointly likelihood and the weighted majority fusion classifiers, that are derived using two different definitions of the minimum expected cost. Without any overhead incurred by multitemporal processing, a user-selected conventional pixelwise classifier makes local class decisions separately using each temporal d...

The Supervised Machine Learning task of classification has parallels with Information Retrieval (IR): in each case, items (documents in the case of IR) are required to be categorised into discrete classes (relevant or non-relevant). Thus a parallel can also be drawn between classifier ensembles, where evidence from multiple classifiers are combined to achieve a superior result, and the IR data ...

The contribution of this paper is threefold: (1) to formulate a decision fusion problem encountered in the design of a multi-modal identity verification system as a particular classification problem, (2) to propose three simple classifiers to solve this problem, (3) to compare the relative performances of the proposed classifiers. The multi-modal identity verification system under consideration...

Fusion different biometrics is an effective way to design a biometric system with robust performance. To do this, normalization functions are employed. However, these functions can not follow the distributions of scores from distinct classifiers. Consequently different normalization errors are introduced. In this paper, the scores from different classifiers are converted into the corresponding ...