We present a novel approach to text categorisation with the aid of the Mahalanobis distance measure for classification. For correlated datasets, classification using the Euclidean distance is not very accurate. The use of the Mahalanobis distance exploits the correlation in data for the purpose of classification. For achieving this on large datasets, an unsupervised dimensionality reduction tec...

Multi-task learning (MTL) has been shown to improve prediction performance in a number of different contexts by learning models jointly on multiple different, but related tasks. Network data, which are a priori data with a rich relational structure, provide an important context for applying MTL. In particular, the explicit relational structure implies that network data is not i.i.d. data. Netwo...

Shape priors have been widely used for level set-based tracking to solve some difficult problems, such as noisy data, partial occlusions and weak contrast at the boundaries. In this paper, we propose a two-layer hierarchical level set-based tracking framework in which color and shape information are fused sequentially. In the first layer, the initial contour is evolved only with the color featu...

This paper systematically explores the capabilities of different forms of Dynamic Time Warping (DTW) algorithms and their parameter configurations in recognising whole-of-body gestures. The standard DTW (SDTW) (Sakoe and Chiba 1978), globally feature weighted DTW (Reyes et al. 2011) and locally feature weighted DTW (Arici et al. 2013) algorithms are particularly considered, while an enhanced ve...

The distance or similarity metric plays an important role in many natural language processing (NLP) tasks. Previous studies have demonstrated the effectiveness of a number of metrics such as the Jaccard coefficient, especially in synonym acquisition. While the existing metrics perform quite well, to further improve performance, we propose the use of a supervised machine learning algorithm that ...

We present an evaluation of a probabilistic, part-based algorithm designed at The Ohio State University. Our algorithm is robust to errors of precision made by the (automatic) face and facial feature detector and to local image changes due to, for example, expression and illumination. Our contributions include the design of a novel face and facial feature detector and the justification of the u...

For many pattern recognition methods, high recognition accuracy is obtained at very high expense of computational cost. In this paper, a new algorithm that reduces the computational cost for calculating discriminant function is proposed. This algorithm consists of two stages which are feature vector division and dimensional reduction. The processing of feature division is based on characteristi...

facial feature localisation. The work here uses a basic graph model (three vertices and three arcs) to locate the inner eye corners and the nose tip simultaneously. We intend to extend this to a larger set of the eleven features that exist in our ground truth of the Face Recognition Grand Challenge (FRGC) database. We apply the structural matching algorithm " relaxation by elimination " using a...

This paper describes a vision and 3D laser based registration approach which utilizes visual features to identify correspondences. Visual features are obtained from the images of a standard color camera and the depth of these features is determined by interpolating between the scanning points of a 3D laser range scanner, taking into consideration the visual information in the neighbourhood of t...

In previous work, semi-supervised Fuzzy c-means (ssFCM) was used as an automatic classification technique to classify the Nottingham Tenovus Breast Cancer (NTBC) dataset as no method to do this currently exists. However, the results were poor when compared with semi-manual classification. It is known that the NTBC data is highly non-normal and it was suspected that this affected the poor result...