Distributional Feature Mapping in Data Classification

Performance of a machine learning algorithm depends on the representation of the input data. In computer vision problems, histogram based feature representation has significantly improved the classification tasks. L1 normalized histograms can be modelled by Dirichlet and related distributions to transform input space to feature space. We propose a mapping technique that contains prior knowledge about the distribution of the data and increases the discriminative power of the classifiers in supervised learning such as Support Vector Machine (SVM). The mapping technique for proportional data which is based on Dirichlet, Generalized Dirichlet, Beta Liouville, scaled Dirichlet and shifted scaled Dirichlet distributions can be incorporated with traditional kernels to improve the base kernels accuracy. Experimental results show that the proposed technique for proportional data increases accuracy for machine vision tasks such as natural scene recognition, satellite image classification, gender classification, facial expression recognition and human action recognition in videos. In addition, in object tracking, learning parametric features of the target object using Dirichlet and related distributions may help to capture representations invariant to noise. This further motivated our study of such distributions in object tracking. We propose a framework for feature representation on probability simplex for proportional data utilizing the histogram representation of the target object at initial frame. A set of parameter vectors determine the appearance features of the target object in the subsequent frames. Motivated by the success of distribution based feature mapping for proportional data, we extend this technique for semi-bounded data utilizing inverted Dirichlet, generalized inverted Dirichlet and inverted Beta Liouville distributions. Similar approach is taken into account for count data where Dirichlet multinomial and generalized Dirichlet multinomial distributions are used to map density features with input features

An investigation of techniques in deformable object recognition

The human\u27s innate ability to process information garnered from a visual scene has no parallel in the digital realm. This task is taken for granted in human cognition, but has not been met by a complete digital solution even following years of research. This difficulty can be explained by the shear complexity of the physology of the visual pathway. Although a complete solution has not been created, there are a number of examples of solutions that address parts of the problem. The recognition of deformable objects is the area addressed in this work. The specific task researched was the recognition of creatures in structured visual scenes. The focus was on developing a set of features which are able to differentiate between target creature classes. The implications of this research lie in ecoinformatics and field biology with the automated collection and annotation of biological data. The thesis will present a survey of the current literature addressing techniques which have been used to solve similar problems. An algorithm to perform the recognition will be presented and the results discussed. Finally, potential areas for improvement will be described

Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds

Sparsity-based representations have recently led to notable results in various visual recognition tasks. In a separate line of research, Riemannian manifolds have been shown useful for dealing with features and models that do not lie in Euclidean spaces. With the aim of building a bridge between the two realms, we address the problem of sparse coding and dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping. This in turn enables us to extend two sparse coding schemes to Grassmann manifolds. Furthermore, we propose closed-form solutions for learning a Grassmann dictionary, atom by atom. Lastly, to handle non-linearity in data, we extend the proposed Grassmann sparse coding and dictionary learning algorithms through embedding into Hilbert spaces. Experiments on several classification tasks (gender recognition, gesture classification, scene analysis, face recognition, action recognition and dynamic texture classification) show that the proposed approaches achieve considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelized Affine Hull Method and graph-embedding Grassmann discriminant analysis.Comment: Appearing in International Journal of Computer Visio