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2010

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Articles 241 - 243 of 243

Full-Text Articles in Databases and Information Systems

Dual Phase Learning For Large Scale Video Gait Recognition, Jialie Shen, Hwee Hwa Pang, Dacheng Tao, Xuelong Li Jan 2010

Dual Phase Learning For Large Scale Video Gait Recognition, Jialie Shen, Hwee Hwa Pang, Dacheng Tao, Xuelong Li

Research Collection School Of Computing and Information Systems

Accurate gait recognition from video is a complex process involving heterogenous features, and is still being developed actively. This article introduces a novel framework, called GC2F, for effective and efficient gait recognition and classification. Adopting a ”refinement-and-classification” principle, the framework comprises two components: 1) a classifier to generate advanced probabilistic features from low level gait parameters; and 2) a hidden classifier layer (based on multilayer perceptron neural network) to model the statistical properties of different subject classes. To validate our framework, we have conducted comprehensive experiments with a large test collection, and observed significant improvements in identification accuracy relative to …


Cbtv: Visualising Case Bases For Similarity Measure Design And Selection, Brian Mac Namee, Sarah Jane Delany Jan 2010

Cbtv: Visualising Case Bases For Similarity Measure Design And Selection, Brian Mac Namee, Sarah Jane Delany

Conference papers

In CBR the design and selection of similarity measures is paramount. Selection can benefit from the use of exploratory visualisation- based techniques in parallel with techniques such as cross-validation ac- curacy comparison. In this paper we present the Case Base Topology Viewer (CBTV) which allows the application of different similarity mea- sures to a case base to be visualised so that system designers can explore the case base and the associated decision boundary space. We show, using a range of datasets and similarity measure types, how the idiosyncrasies of particular similarity measures can be illustrated and compared in CBTV allowing …


Inside The Selection Box: Visualising Active Learning Selection Strategies, Brian Mac Namee, Rong Hu, Sarah Jane Delany Jan 2010

Inside The Selection Box: Visualising Active Learning Selection Strategies, Brian Mac Namee, Rong Hu, Sarah Jane Delany

Conference papers

Visualisations can be used to provide developers with insights into the inner workings of interactive machine learning techniques. In active learning, an inherently interactive machine learning technique, the design of selection strategies is the key research question and this paper demonstrates how spring model based visualisations can be used to provide insight into the precise operation of various selection strategies. Using sample datasets, this paper provides detailed examples of the differences between a range of selection strategies.