We propose to use new SVM-type classifiers in a binary hierarchical tree classification structure to efficiently address the multi-class classification problem. A new hierarchical design method, WSV (weighted support vector) K-means Clustering, is presented; it automatically selects the classes to be separated at each node in the hierarchy. Our method is able to visualize and cluster high-dimensional support vector data; therefore, it improves upon prior hierarchical classifier designs. At each node in the hierarchy, we apply an SVRDM (support vector representation and discrimination machine) classifier, which offers generalization and good rejection of unseen false objects; rejection is not achieved with the standard SVM classifier. We provide the theoretical basis and insight into the choice of the Gaussian kernel to provide the SVRDM's rejection ability. New classification and rejection test results are presented on a real IR (infra-red) database.
In most ATR applications, objects are not only present with thermal and aspect view angle variations, its size (range)
also changes as the sensor approaches the target, and depression angle variations can exist. Therefore, it is important and
realistic to know how to handle these variations. We apply our new SVRDM (support vector representation and
discrimination machine) classifier to address these problems. The SVRDM classifier has good generalization (like the
standard SVM does), and it has the added property of a good rejection ability. In other words, it not only gives very
promising recognition results on the true target classes, it is also able to reject other unseen objects (referred to as
confusers). We address the following variation issues: the scale range one SVRDM can recognize when trained on data
at one or more ranges, the depression angle difference one SVRDM can recognize when trained on data at only one (or
several) depression angles, and the number of aspect views needed to be included in the training set to handle recognition
of targets with aspect variations, and the classification and rejection performance. Thus, our results are most unique and
worthwhile but are not easily compared to prior work. Recognition and rejection test results are presented on both
simulated and real infra-red (IR) data.
We propose a binary hierarchical classifier to solve the multi-class classification problem with aspect variations in objects and with rejection of non-object false targets. The hierarchical architecture design is automated using our new k-means SVRM (support vector representation machine) clustering algorithm. At each node in the hierarchy, we use a new SVRDM (support vector representation and discrimination machine) classifier, which has good generalization and offers good rejection ability. We also provide a theoretical basis for our choice of kernel function (K), and our method of parameter selection (for σ and p). Using this hierarchical SVRDM classifier with magnitude Fourier transform features, experimental results on both simulated and real infra-red (IR) databases are excellent.
Prior studies of multimodal biometric fusion have shown that it can improve performance over use of a single unimodal biometric. The well-known multimodal methods do not consider the quality information of the data used when combining the results from different matchers. In addition to examining these well-known fusion methods, we introduce novel methods of fusion which combine face and fingerprint biometric results using fingerprint data quality information. We show that multimodal biometric fusion using data quality information outperforms standard multimodal results and unimodal systems.
A hierarchical classifier using a new SVRDM (support vector representation and discrimination machine) is proposed for automatic target recognition. Shift and scale-invariant features are considered. In addition, we consider the ability of the classifier to reject non-object class or clutter inputs. Initial recognition and rejection test results on infra-red (IR) data are excellent.