Multiple classifier boosting and tree-structured classifiers

Visual recognition problems often involve classification of myriads of pixels, across scales, to locate objects of interest in an image or to segment images according to object classes. The requirement for high speed and accuracy makes the problems very challenging and has motivated studies on efficient classification algorithms. A novel multi-classifier boosting algorithm is proposed to tackle the multimodal problems by simultaneously clustering samples and boosting classifiers in Section 2. The method is extended into an online version for object tracking in Section 3. Section 4 presents a tree-structured classifier, called Super tree, to further speed up the classification time of a standard boosting classifier. The proposed methods are demonstrated for object detection, tracking and segmentation tasks. © 2013 Springer-Verlag Berlin Heidelberg.

Probabilistic graphical models for semi-supervised traffic classification

Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features. Copyright © 2010 ACM.

The unsteady development of a turbulent wake through a downstream low-pressure turbine blade passage

This paper presents two-dimensional LDA measurements of the convection of a wake through a low-pressure (LP) turbine cascade. Previous studies have shown the wake convection to be kinematic but have not provided details of the turbulent field. The spatial resolution of these measurements has facilitated the calculation of the production of turbulent kinetic energy and this has revealed a mechanism for turbulence production as the wake converts through the bladerow. The measured ensemble-averaged velocity field confirmed the previously reported kinematics of wake convection while the measurements of the turbulence quantities showed the wake fluid to be characterised by elevated levels of turbulent kinetic energy (TKE) and to have an anisotropic structure. Based on the measured mean and turbulence quantities, the production of turbulent kinetic energy was calculated. This highlighted a TKE production mechanism that resulted in increased levels of turbulence over the rear suction surface where boundary layer transition occurs. The turbulence production mechanism within the bladerow was also observed to produce more nearly isotropic turbulence. Production occurs when the principal stresses within the wake are aligned with the mean strains. This coincides with the maximum distortion of the wake within the blade passage and provides a mechanism for the production of turbulence outside of the boundary layer.

Online multiple classifier boosting for object tracking

This paper presents a new online multi-classifier boosting algorithm for learning object appearance models. In many cases the appearance model is multi-modal, which we capture by training and updating multiple strong classifiers. The proposed algorithm jointly learns the classifiers and a soft partitioning of the input space, defining an area of expertise for each classifier. We show how this formulation improves the specificity of the strong classifiers, allowing simultaneous location and pose estimation in a tracking task. The proposed online scheme iteratively adapts the classifiers during tracking. Experiments show that the algorithm successfully learns multi-modal appearance models during a short initial training phase, subsequently updating them for tracking an object under rapid appearance changes. © 2010 IEEE.

Cross-Lingual spoken language understanding from unaligned data using discriminative classification models and machine translation

This paper investigates several approaches to bootstrapping a new spoken language understanding (SLU) component in a target language given a large dataset of semantically-annotated utterances in some other source language. The aim is to reduce the cost associated with porting a spoken dialogue system from one language to another by minimising the amount of data required in the target language. Since word-level semantic annotations are costly, Semantic Tuple Classifiers (STCs) are used in conjunction with statistical machine translation models both of which are trained from unaligned data to further reduce development time. The paper presents experiments in which a French SLU component in the tourist information domain is bootstrapped from English data. Results show that training STCs on automatically translated data produced the best performance for predicting the utterance's dialogue act type, however individual slot/value pairs are best predicted by training STCs on the source language and using them to decode translated utterances. © 2010 ISCA.

Energy scattering in weakly non-linear systems.

The Chinese Tam-Tam exhibits non-linear behavior in its vibro-acoustic response. The frequency content of the response during free, unforced vibration smoothly changes, with energy being progressively smeared out over a greater bandwidth with time. This is used as a motivating case for the general study of the phenomenon of energy cascading through weak nonlinearity. Numerical models based upon the Fermi-Pasta-Ulam system of non-linearly coupled oscillators, modified with the addition of damping, have been developed. These were used to study the response of ensembles of systems with randomized natural frequencies. Results from simulations will be presented here. For un-damped systems, individual ensemble members exhibit cyclical energy exchange between linear modes, but the ensemble average displays a steady state. For the ensemble response of damped systems, lightly damped modes can exhibit an effective damping which is higher than predicated by linear theory. The presence of a non-linearity provides a path for energy flow to other modes, increasing the apparent damping spectrum at some frequencies and reducing it at others. The target of this work is a model revealing the governing parameters of a generic system of this type and leading to predictions of the ensemble response.

Derivative Kernels for Noise Robust ASR

Recently there has been interest in combined gen- erative/discriminative classifiers. In these classifiers features for the discriminative models are derived from generative kernels. One advantage of using generative kernels is that systematic approaches exist how to introduce complex dependencies beyond conditional independence assumptions. Furthermore, by using generative kernels model-based compensation/adaptation tech- niques can be applied to make discriminative models robust to noise/speaker conditions. This paper extends previous work with combined generative/discriminative classifiers in several directions. First, it introduces derivative kernels based on context- dependent generative models. Second, it describes how derivative kernels can be incorporated in continuous discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high- dimensional features of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task.

Derivative kernels for noise robust ASR

Recently there has been interest in combining generative and discriminative classifiers. In these classifiers features for the discriminative models are derived from the generative kernels. One advantage of using generative kernels is that systematic approaches exist to introduce complex dependencies into the feature-space. Furthermore, as the features are based on generative models standard model-based compensation and adaptation techniques can be applied to make discriminative models robust to noise and speaker conditions. This paper extends previous work in this framework in several directions. First, it introduces derivative kernels based on context-dependent generative models. Second, it describes how derivative kernels can be incorporated in structured discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high-dimensional feature-spaces of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task. © 2011 IEEE.

The deposition of small particles from a turbulent air flow in a curved duct

The paper describes an experimental and theoretical study of the deposition of small spherical particles from a turbulent air flow in a curved duct. The objective was to investigate the interaction between the streamline curvature of the primary flow and the turbulent deposition mechanisms of diffusion and turbophoresis. The experiments were conducted with particles of uranine (used as a fluorescent tracer) produced by an aerosol generator. The particles were entrained in an air flow which passed vertically downwards through a long straight channel of rectangular cross-section leading to a 90° bend. The inside surfaces of the channel and bend were covered with tape to collect the deposited particles. Following a test run the tape was removed in sections, the uranine was dissolved in sodium hydroxide solution and the deposition rates established by measuring the uranine concentration with a luminescence spectrometer. The experimental results were compared with calculations of particle deposition in a curved duct using a computer program that solved the ensemble-averaged particle mass and momentum conservation equations. A particle density-weighted averaging procedure was used and the equations were expressed in terms of the particle convective, rather than total, velocity. This approach provided a simpler formulation of the particle turbulence correlations generated by the averaging process. The computer program was used to investigate the distance required to achieve a fully-developed particle flow in the straight entry channel as well as the variation of the deposition rate around the bend. The simulations showed good agreement with the experimental results. © 2012 Elsevier Ltd.