Concurrent Viola Jones classifiers on a portable Beowulf cluster : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University

Real-time Computer Vision is an interesting application for supercomputing, real-time applications (vision processing in particular) employ special purpose hardware such as DSPs to achieve high performance. This thesis explores parallel computers particularly commodity general purpose hardware. We also build a prototype to better understand the economics of supercomputing, specifically related to mobile computing - low power, rugged design by building a mobile computer. A new communication layer is built, where by the nature of the locality of the nodes allows one to optimise the protocols to reduce the latency comparably. Finally a study and in depth results of the algorithm, the Viola Jones Object detector in parallel are presented followed by reflection and future work based on the current results and platform

iCaRL: Incremental Classifier and Representation Learning

A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively. iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail.Comment: Accepted paper at CVPR 201

Designing an automated prototype tool for preservation quality metadata extraction for ingest into digital repository

We present a viable framework for the automated extraction of preservation quality metadata, which is adjusted to meet the needs of, ingest to digital repositories. It has three distinctive features: wide coverage, specialisation and emphasis on quality. Wide coverage is achieved through the use of a distributed system of tool repositories, which helps to implement it over a broad range of document object types. Specialisation is maintained through the selection of the most appropriate metadata extraction tool for each case based on the identification of the digital object genre. And quality is sustained by introducing control points at selected stages of the workflow of the system. The integration of these three features as components in the ingest of material into digital repositories is a defining step ahead in the current quest for improved management of digital resources

Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales

Predicting the class of a customer profile is a key task in marketing, which enables businesses to approach the right customer with the right product at the right time through the right channel to satisfy the customer's evolving needs. However, due to costs, privacy and/or data protection, only the business' owned transactional data is typically available for constructing customer profiles. Predicting the class of customer profiles based on such data is challenging, as the data tends to be very large, heavily sparse and highly skewed. We present a new approach that is designed to efficiently and accurately handle the multi-class classification of customer profiles built using sparse and skewed transactional data. Our approach first bins the customer profiles on the basis of the number of items transacted. The discovered bins are then partitioned and prototypes within each of the discovered bins selected to build the multi-class classifier models. The results obtained from using four multi-class classifiers on real-world transactional data from the food sales domain consistently show the critical numbers of items at which the predictive performance of customer profiles can be substantially improved