One-class classifiers based on entropic spanning graphs

One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach takes into account the possibility to process also non-numeric data by means of an embedding procedure. The spanning graph is learned on the embedded input data and the outcoming partition of vertices defines the classifier. The final partition is derived by exploiting a criterion based on mutual information minimization. Here, we compute the mutual information by using a convenient formulation provided in terms of the $\alpha$-Jensen difference. Once training is completed, in order to associate a confidence level with the classifier decision, a graph-based fuzzy model is constructed. The fuzzification process is based only on topological information of the vertices of the entropic spanning graph. As such, the proposed one-class classifier is suitable also for data characterized by complex geometric structures. We provide experiments on well-known benchmarks containing both feature vectors and labeled graphs. In addition, we apply the method to the protein solubility recognition problem by considering several representations for the input samples. Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of-the-art approaches.Comment: Extended and revised version of the paper "One-Class Classification Through Mutual Information Minimization" presented at the 2016 IEEE IJCNN, Vancouver, Canad

Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors

This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods

A broad typology of dry rainforests on the western slopes of New South Wales

Dry rainforests are those communities that have floristic and structural affinities to mesic rainforests and occur in parts of eastern and northern Australia where rainfall is comparatively low and often highly seasonal. The dry rainforests of the western slopes of New South Wales are poorly-understood compared to other dry rainforests in Australia, due to a lack of regional scale studies. This paper attempts to redress this by deriving a broad floristic and structural typology for this vegetation type. Phytogeographical analysis followed full floristic surveys conducted on 400 m2 plots located within dry rainforest across the western slopes of NSW. Cluster analysis and ordination of 208 plots identified six floristic groups. Unlike in some other regional studies of dry rainforest these groups were readily assigned to Webb structural types, based on leaf size classes, leaf retention classes and canopy height. Five community types were described using both floristic and structural data: 1) Ficus rubiginosa–Notelaea microcarpa notophyll vine thicket, 2) Ficus rubiginosa–Alectryon subcinereus–Notelaea microcarpa notophyll vine forest, 3) Elaeodendron australe–Notelaea microcarpa–Geijera parviflora notophyll vine thicket, 4) Notelaea microcarpa– Geijera parviflora–Ehretia membranifolia semi-evergreen vine thicket, and 5) Cadellia pentastylis low microphyll vine forest. Floristic groupings were consistent with those described by previous quantitative studies which examined smaller portions of this study area. There was also general agreement between the present analytical study and a previous intuitive classification of dry rainforest vegetation throughout the study area, but little concurrence with a continental scale floristic classification of rainforest