A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

Multimodal Affect Recognition: Current Approaches and Challenges

Many factors render multimodal affect recognition approaches appealing. First, humans employ a multimodal approach in emotion recognition. It is only fitting that machines, which attempt to reproduce elements of the human emotional intelligence, employ the same approach. Second, the combination of multiple-affective signals not only provides a richer collection of data but also helps alleviate the effects of uncertainty in the raw signals. Lastly, they potentially afford us the flexibility to classify emotions even when one or more source signals are not possible to retrieve. However, the multimodal approach presents challenges pertaining to the fusion of individual signals, dimensionality of the feature space, and incompatibility of collected signals in terms of time resolution and format. In this chapter, we explore the aforementioned challenges while presenting the latest scholarship on the topic. Hence, we first discuss the various modalities used in affect classification. Second, we explore the fusion of modalities. Third, we present publicly accessible multimodal datasets designed to expedite work on the topic by eliminating the laborious task of dataset collection. Fourth, we analyze representative works on the topic. Finally, we summarize the current challenges in the field and provide ideas for future research directions

Image processing system based on similarity/dissimilarity measures to classify binary images from contour-based features

Image Processing Systems (IPS) try to solve tasks like image classification or segmentation based on its content. Many authors proposed a variety of techniques to tackle the image classification task. Plenty of methods address the performance of the IPS [1], as long as the influence of many external circumstances, such as illumination, rotation, and noise [2]. However, there is an increasing interest in classifying shapes from binary images (BI). Shape Classification (SC) from BI considers a segmented image as a sample (backgroundsegmentation [3]) and aims to identify objects based in its shape..

Image processing system based on similarity/dissimilarity measures to classify binary images from contour-based features

Image Processing Systems (IPS) try to solve tasks like image classification or segmentation based on its content. Many authors proposed a variety of techniques to tackle the image classification task. Plenty of methods address the performance of the IPS [1], as long as the influence of many external circumstances, such as illumination, rotation, and noise [2]. However, there is an increasing interest in classifying shapes from binary images (BI). Shape Classification (SC) from BI considers a segmented image as a sample (backgroundsegmentation [3]) and aims to identify objects based in its shape..

Deep Learning on Lie Groups for Skeleton-based Action Recognition

In recent years, skeleton-based action recognition has become a popular 3D classification problem. State-of-the-art methods typically first represent each motion sequence as a high-dimensional trajectory on a Lie group with an additional dynamic time warping, and then shallowly learn favorable Lie group features. In this paper we incorporate the Lie group structure into a deep network architecture to learn more appropriate Lie group features for 3D action recognition. Within the network structure, we design rotation mapping layers to transform the input Lie group features into desirable ones, which are aligned better in the temporal domain. To reduce the high feature dimensionality, the architecture is equipped with rotation pooling layers for the elements on the Lie group. Furthermore, we propose a logarithm mapping layer to map the resulting manifold data into a tangent space that facilitates the application of regular output layers for the final classification. Evaluations of the proposed network for standard 3D human action recognition datasets clearly demonstrate its superiority over existing shallow Lie group feature learning methods as well as most conventional deep learning methods.Comment: Accepted to CVPR 201