Letting go of Neo-Liberalism (with some help from Michel Foucault)

Neo-liberalism has become one of the boom concepts of our time. From its original reference point as a descriptor of the economics of the “Chicago School” such as Milton Friedman, or authors such as Friedrich von Hayek, neo-liberalism has become an all-purpose descriptor and explanatory device for phenomena as diverse as Bollywood weddings, standardized testing in schools, violence in Australian cinema, and the digitization of content in public libraries. Moreover, it has become an entirely pejorative term: no-one refers to their own views as “neo-liberal”, but it rather refers to the erroneous views held by others, whether they acknowledge this or not. Neo-liberalism as it has come to be used, then, bears many of the hallmarks of a dominant ideology theory in the classical Marxist sense, even if it is often not explored in these terms. This presentation will take the opportunity provided by the English language publication of Michel Foucault’s 1978-79 lectures, under the title of The Birth of Biopolitics, to consider how he used the term neo-liberalism, and how this equates with its current uses in critical social and cultural theory. It will be argued that Foucault did not understand neo-liberalism as a dominant ideology in these lectures, but rather as marking a point of inflection in the historical evolution of liberal political philosophies of government. It will also be argued that his interpretation of neo-liberalism was more nuanced and more comparative than the more recent uses of Foucault in the literature on neo-liberalism. It will also look at how Foucault develops comparative historical models of liberal capitalism in The Birth of Biopolitics, arguing that this dimension of his work has been lost in more recent interpretations, which tend to retro-fit Foucault to contemporary critiques of either U.S. neo-conservatism or the “Third Way” of Tony Blair’s New Labour in the UK.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Michel Foucault's 'the birth of biopolitics' and contemporary neo-liberalism debates

Neo-liberalism has become one of the boom concepts of our time. From its original reference point as a descriptor of the economics of the ‘Chicago School’ or authors such as Friedrich von Hayek, neo-liberalism has become an all-purpose concept, explanatory device and basis for social critique. This presentation evaluates Michel Foucault’s 1978–79 lectures, published as The Birth of Biopolitics, to consider how he used the term neo-liberalism, and how this equates with its current uses in critical social and cultural theory. It will be argued that Foucault did not understand neo-liberalism as a dominant ideology in these lectures, but rather as marking a point of inflection in the historical evolution of liberal political philosophies of government. It will also be argued that his interpretation of neo-liberalism was more nuanced and more comparative than more recent contributions. The article points towards an attempt to theorize comparative historical models of liberal capitalism.

Re-framing fashion : from original and copy to adaptation

This article discusses the interaction between original and adaptation in the fashion system; the study also analyses, at a micro level, practices of adaptation adopted by consumers when making and re-making fashionable clothes. The article shows that the distinction between original and copy is historically determined as it grew out of the romantic notion of the authentic work of art. This article suggests that, in the impossibility to determine copyright in fashion, adaptation is a better descriptor of practices that transform garments; the concept of adaptation also abolishes trite notions of fashion as pastiche or bricolage, arguing for as a way to look at the many variations and re-contextualisations of garments historically and cross-culturally.

Australian ePortfolio Project–Stage 2 ePortfolio use by university students in Australia : Developing a sustainable community of practice Case studies extracted from the final project report December 2009

Fourteen sase studies extracted from the final project report - December 2009 Australian Flexible Learning Framework: E-portfolios Community of Practice (Aus) Personal learning plans and ePortfolio (Aus) RMIT University: Introducing ePortfolios (Aus) ePortfolio Practice: ALTC Exchange (Aus) Australian PebblePad User Group (APpUG) (Aus) ePortfolios in the library and information services sector (Aus) PDP and ePortfolios UK (UK) SURF NL Portfolio (Netherlands) University of Canterbury ePortfolio (NZ) AAEEBL: Association for Authentic, Experiential and Evidence-Based Learning (USA) Midlands Eportfolio Group, West Midlands(UK) EPAC: Electronic Portfolio Action and Communication (USA) Scottish Higher Education PDP Forum (UK) Centre for Recording Achievement (CRA)(UK)

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Heroes & harmony

The first practice-led research project 'Heroes of the Club', through collaboration with business and community, involved portraying the stories on canvas of heroes of the Australian Surf Life Saving movement. The second project 'Crossing the intersection… art and life' researched a post-modernist approach to a fusion of the aesthetics of Abstract Expressionism and Pop Art. This study resulted in a body of work 'Long Playing' which sought to harmoniously reconcile two apparent polarities of style and context between high and low art characteristics, through personal narrative and with reference to artistic tradition.

Unusual Event Detection in Crowded Scenes Using Bag of LBPs in spatio-temporal patches

Modelling events in densely crowded environments remains challenging, due to the diversity of events and the noise in the scene. We propose a novel approach for anomalous event detection in crowded scenes using dynamic textures described by the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) descriptor. The scene is divided into spatio-temporal patches where LBP-TOP based dynamic textures are extracted. We apply hierarchical Bayesian models to detect the patches containing unusual events. Our method is an unsupervised approach, and it does not rely on object tracking or background subtraction. We show that our approach outperforms existing state of the art algorithms for anomalous event detection in UCSD dataset.

Challenges facing sustainable housing in Saudi Arabia : a current study showing the level of public awareness

Sustainable housing implementation requires strong support from the public, government and the housing industry. Lack of public awareness and understanding of the language and the meaning of sustainable housing may cause lack of public support. Salama stated that "sustainability or sustainable design is simply a rephrasing of some of the forgotten values of traditional architecture and urbanism"(Salama 2007). This exploratory paper examines public awareness of sustainable housing in Saudi Arabia. In developing countries, like Saudi Arabia, which have been experiencing a rapid rate of urbanisation, sustainable concept intervention is essential due to the scarcity of resources (Reffat 2004a). Sustainable building methods include the full use of the site design, passive solar design, natural light and ventilation. This paper reports on an exploratory survey on understanding the potential of the implementation of sustainable housing in Saudi Arabia. The main problem is that more than half of respondents were not aware of sustainable housing. Thus, one of the recommendations from the survey is to educate the public by using local media to inform people of the benefits of sustainable implementation to both new and existing housing stock.

Negative determinant of Hessian features

Local image feature extractors that select local maxima of the determinant of Hessian function have been shown to perform well and are widely used. This paper introduces the negative local minima of the determinant of Hessian function for local feature extraction. The properties and scale-space behaviour of these features are examined and found to be desirable for feature extraction. It is shown how this new feature type can be implemented along with the existing local maxima approach at negligible extra processing cost. Applications to affine covariant feature extraction and sub-pixel precise corner extraction are demonstrated. Experimental results indicate that the new corner detector is more robust to image blur and noise than existing methods. It is also accurate for a broader range of corner geometries. An affine covariant feature extractor is implemented by combining the minima of the determinant of Hessian with existing scale and shape adaptation methods. This extractor can be implemented along side the existing Hessian maxima extractor simply by finding both minima and maxima during the initial extraction stage. The minima features increase the number of correspondences by two to four fold. The additional minima features are very distinct from the maxima features in descriptor space and do not make the matching process more ambiguous.

Class contestations and Australia’s resource boom : the emergence of the ‘cashed-up bogan’

This article examines the figure of the ‘Cashed-up Bogan’ or ‘Cub’ in Australian media from 2006 to 2009. It explains that ‘Bogan’, like that of ‘Chav’ in Britain, is a widely engaged negative descriptor for the white working-class poor. In contrast, ‘Cubs’ have economic capital. This capital, and the Cub’s emergence, is linked to Australia’s resource boom of recent decades when the need for skilled labour allowed for a highly demarcated segment of the working class to earn relatively high incomes in the mining sector and to participate in consumption. We argue that access to economic capital has provided the Cub with mobility to enter the everyday spaces of the middle class, but this has caused disruption and anxiety to middle-class hegemony. As a result, the middle class has redrawn and reinforced class-infused symbolic and cultural boundaries, whereby, despite their wealth, pernicious media representations mark Cubs as ‘other’ to the middle-class deservingness, taste and morality.

In the pursuit of effective affective computing : the relationship between features and registration

For facial expression recognition systems to be applicable in the real world, they need to be able to detect and track a previously unseen person's face and its facial movements accurately in realistic environments. A highly plausible solution involves performing a "dense" form of alignment, where 60-70 fiducial facial points are tracked with high accuracy. The problem is that, in practice, this type of dense alignment had so far been impossible to achieve in a generic sense, mainly due to poor reliability and robustness. Instead, many expression detection methods have opted for a "coarse" form of face alignment, followed by an application of a biologically inspired appearance descriptor such as the histogram of oriented gradients or Gabor magnitudes. Encouragingly, recent advances to a number of dense alignment algorithms have demonstrated both high reliability and accuracy for unseen subjects [e.g., constrained local models (CLMs)]. This begs the question: Aside from countering against illumination variation, what do these appearance descriptors do that standard pixel representations do not? In this paper, we show that, when close to perfect alignment is obtained, there is no real benefit in employing these different appearance-based representations (under consistent illumination conditions). In fact, when misalignment does occur, we show that these appearance descriptors do work well by encoding robustness to alignment error. For this work, we compared two popular methods for dense alignment-subject-dependent active appearance models versus subject-independent CLMs-on the task of action-unit detection. These comparisons were conducted through a battery of experiments across various publicly available data sets (i.e., CK+, Pain, M3, and GEMEP-FERA). We also report our performance in the recent 2011 Facial Expression Recognition and Analysis Challenge for the subject-independent task.

Robust facial feature extraction and matching

Automated feature extraction and correspondence determination is an extremely important problem in the face recognition community as it often forms the foundation of the normalisation and database construction phases of many recognition and verification systems. This paper presents a completely automatic feature extraction system based upon a modified volume descriptor. These features form a stable descriptor for faces and are utilised in a reversible jump Markov chain Monte Carlo correspondence algorithm to automatically determine correspondences which exist between faces. The developed system is invariant to changes in pose and occlusion and results indicate that it is also robust to minor face deformations which may be present with variations in expression.

Towards spontaneous facial expression recognition in real-world video

Facial expression is an important channel of human social communication. Facial expression recognition (FER) aims to perceive and understand emotional states of humans based on information in the face. Building robust and high performance FER systems that can work in real-world video is still a challenging task, due to the various unpredictable facial variations and complicated exterior environmental conditions, as well as the difficulty of choosing a suitable type of feature descriptor for extracting discriminative facial information. Facial variations caused by factors such as pose, age, gender, race and occlusion, can exert profound influence on the robustness, while a suitable feature descriptor largely determines the performance. Most present attention on FER has been paid to addressing variations in pose and illumination. No approach has been reported on handling face localization errors and relatively few on overcoming facial occlusions, although the significant impact of these two variations on the performance has been proved and highlighted in many previous studies. Many texture and geometric features have been previously proposed for FER. However, few comparison studies have been conducted to explore the performance differences between different features and examine the performance improvement arisen from fusion of texture and geometry, especially on data with spontaneous emotions. The majority of existing approaches are evaluated on databases with posed or induced facial expressions collected in laboratory environments, whereas little attention has been paid on recognizing naturalistic facial expressions on real-world data. This thesis investigates techniques for building robust and high performance FER systems based on a number of established feature sets. It comprises of contributions towards three main objectives: (1) Robustness to face localization errors and facial occlusions. An approach is proposed to handle face localization errors and facial occlusions using Gabor based templates. Template extraction algorithms are designed to collect a pool of local template features and template matching is then performed to covert these templates into distances, which are robust to localization errors and occlusions. (2) Improvement of performance through feature comparison, selection and fusion. A comparative framework is presented to compare the performance between different features and different feature selection algorithms, and examine the performance improvement arising from fusion of texture and geometry. The framework is evaluated for both discrete and dimensional expression recognition on spontaneous data. (3) Evaluation of performance in the context of real-world applications. A system is selected and applied into discriminating posed versus spontaneous expressions and recognizing naturalistic facial expressions. A database is collected from real-world recordings and is used to explore feature differences between standard database images and real-world images, as well as between real-world images and real-world video frames. The performance evaluations are based on the JAFFE, CK, Feedtum, NVIE, Semaine and self-collected QUT databases. The results demonstrate high robustness of the proposed approach to the simulated localization errors and occlusions. Texture and geometry have different contributions to the performance of discrete and dimensional expression recognition, as well as posed versus spontaneous emotion discrimination. These investigations provide useful insights into enhancing robustness and achieving high performance of FER systems, and putting them into real-world applications.