Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

Designing Semantic Kernels as Implicit Superconcept Expansions

Recently, there has been an increased interest in the exploitation of background knowledge in the context of text mining tasks, especially text classification. At the same time, kernel-based learning algorithms like Support Vector Machines have become a dominant paradigm in the text mining community. Amongst other reasons, this is also due to their capability to achieve more accurate learning results by replacing standard linear kernel (bag-of-words) with customized kernel functions which incorporate additional apriori knowledge. In this paper we propose a new approach to the design of ‘semantic smoothing kernels’ by means of an implicit superconcept expansion using well-known measures of term similarity. The experimental evaluation on two different datasets indicates that our approach consistently improves performance in situations where (i) training data is scarce or (ii) the bag-ofwords representation is too sparse to build stable models when using the linear kernel

Beyond 2D-grids: a dependence maximization view on image browsing

Ideally, one would like to perform image search using an intuitive and friendly approach. Many existing image search engines, however, present users with sets of images arranged in some default order on the screen, typically the relevance to a query, only. While this certainly has its advantages, arguably, a more flexible and intuitive way would be to sort images into arbitrary structures such as grids, hierarchies, or spheres so that images that are visually or semantically alike are placed together. This paper focuses on designing such a navigation system for image browsers. This is a challenging task because arbitrary layout structure makes it difficult -- if not impossible -- to compute cross-similarities between images and structure coordinates, the main ingredient of traditional layouting approaches. For this reason, we resort to a recently developed machine learning technique: kernelized sorting. It is a general technique for matching pairs of objects from different domains without requiring cross-domain similarity measures and hence elegantly allows sorting images into arbitrary structures. Moreover, we extend it so that some images can be preselected for instance forming the tip of the hierarchy allowing to subsequently navigate through the search results in the lower levels in an intuitive way

Text Mining Infrastructure in R

During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.

Text Categorization Model Based on Linear Support Vector Machine

Spam mails constitute a lot of nuisances in our electronic mail boxes, as they occupy huge spaces which could rather be used for storing relevant data. They also slow down network connection speed and make communication over a network slow. Attackers have often employed spam mails as a means of sending phishing mails to their targets in order to perpetrate data breach attacks and other forms of cybercrimes. Researchers have developed models using machine learning algorithms and other techniques to filter spam mails from relevant mails, however, some algorithms and classifiers are weak, not robust, and lack visualization models which would make the results interpretable by even non-tech savvy people. In this work, Linear Support Vector Machine (LSVM) was used to develop a text categorization model for email texts based on two categories: Ham and Spam. The processes involved were dataset import, preprocessing (removal of stop words, vectorization), feature selection (weighing and selection), development of classification model (splitting data into train (80%) and test sets (20%), importing classifier, training classifier), evaluation of model, deployment of model and spam filtering application on a server (Heroku) using Flask framework. The Agile methodology was adopted for the system design; the Python programming language was implemented for model development. HTML and CSS was used for the development of the web application. The results from the system testing showed that the system had an overall accuracy of 98.56%, recall: 96.5%, F1-score: 97% and F-beta score of 96.23%. This study therefore could be beneficial to e-mail users, to data analysts, and to researchers in the field of NLP