Destination image: identifying baseline perceptions of Brazil, Argentina and Chile in the nascent Australian long haul travel market

While the popularity of destination image research has increased exponentially in the literature, there has been relatively little published about perceptions held by international consumers of destinations in South America. The purpose of this paper is to report the findings of a research project that aimed to identify the baseline market perceptions of Brazil, Argentina and Chile amongst Australian residents, at the time of the emergence of this long haul market. Of interest was the extent to which Australians differentiate the three distinct countries versus perceiving the continent as a gestalt. These baseline perceptions enable the effectiveness of future marketing communications in Australia by the three national tourism offices to be monitored over time. Importance-Performance Analysis (IPA) is used as a practical analytical tool to guide decision makers. In terms of operationalising destination image, a key research finding was the very high ratio or participants using the ‘Don’t know’ (DK) option for each destination performance scale item. This finding has practical implications for the destination marketers, as well as for researchers engaged in destination image research in long haul and/or emerging markets.

Stopover destination image: Using the repertory test to identify salient attributes

This paper reports a rare investigation of stopover destination image. Although the topic of destination image has been one of the most popular in the tourism literature since the 1970s, there has been a lack of research attention in relation to the context of stopover destinations for long haul international travellers. The purpose of this study was to identify attributes deemed salient to Australian consumers when considering stopover destinations for long haul travel to the United Kingdom and Europe. Underpinned by Personal Construct Theory (PCT), the study used the Repertory Test to identify 21 salient attributes, which could be used in the development of a survey instrument to measure the attractiveness of a competitive set of stopover destinations. While the list of attributes shared some commonality with general studies of destination image reported in the literature, the elicitation of a relatively large number of stopover context specific attributes highlights the potential benefit of engaging with consumers in qualitative research, such as using the Repertory Test, during the questionnaire development stage.

Image, Political

Political communication scholars, journalists, and political actors alike, argue that the political process, and deliberative democracy (democracy founded on informed discussion inclusive of citizens), have lost their rational authenticity in that image and media spectacle have become more central to public opinion formation and electoral outcomes than policy. This entry examines the validity of that perception, and the extent to which “image” has emerged as a more significant factor in the political process. And if image is so important in political culture, what the impacts might be on the functioning of democratic processes.

Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs

State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar´ f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.

Visual detection of occluded crop: For automated harvesting

This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.