Log-Euclidean Bag of Words for Human Action Recognition

Representing videos by densely extracted local space-time features has recently become a popular approach for analysing actions. In this paper, we tackle the problem of categorising human actions by devising Bag of Words (BoW) models based on covariance matrices of spatio-temporal features, with the features formed from histograms of optical flow. Since covariance matrices form a special type of Riemannian manifold, the space of Symmetric Positive Definite (SPD) matrices, non-Euclidean geometry should be taken into account while discriminating between covariance matrices. To this end, we propose to embed SPD manifolds to Euclidean spaces via a diffeomorphism and extend the BoW approach to its Riemannian version. The proposed BoW approach takes into account the manifold geometry of SPD matrices during the generation of the codebook and histograms. Experiments on challenging human action datasets show that the proposed method obtains notable improvements in discrimination accuracy, in comparison to several state-of-the-art methods

Feature Extraction: Issues, New Features, and Symbolic Representation

. Feature extraction is an important part of object model acquisition and object recognition systems. Global features describing properties of whole objects, or local features denoting the constituent parts of objects and their relationships may be used. When a model acquisition or object recognition system requires symbolic input, the features should be represented in symbolic form. Global feature extraction is well-known and oft-reported. This paper discusses the issues involved in the extraction of local features, and presents a method to represent them in symbolic form. Some novel features, specifically between two circular arcs, and a line and a circular arc, are also presented. 1 Introduction The information extracted from images and used for object recognition in an image are called features. These features are often matched to similar features in object models, and should be chosen such that they can uniquely identify objects appearing in the images. Features may be global, rep..