Association of health involvement and attitudes towards eating fish on farmed and wild fish consumption in Belgium, Norway and Spain

Consumers in many European countries do not equally meet the recommended daily intake levels for fish consumption. Various factors that can influence fish consumption behaviour have been identified but limited research has been performed on fish consumption behaviour, discriminating between farmed and wild fish. The present survey study confirmed differences in total fish consumption, farmed fish and wild fish consumption behaviour in Belgium, Norway and Spain. Spanish consumers consumed more frequently fish of each category than Norwegian consumers. Belgian consumers reported the lowest consumption frequency of fish. Accordingly, health involvement and attitudes towards fish consumption decreased from Spain over Norway to Belgium, suggesting a positive association of health involvement and attitudes towards fish consumption with total fish consumption. Similar effects were found for farmed and wild fish consumption. In general consumers in all countries were poorly aware of the origin of the fish they consume, despite the mandatory indication of origin on fish labels. Across countries, an increased awareness about fish origin was found with increased fish consumption. The findings of the study indicate that farmed and wild fish consumption should be further stimulated among Belgian, Norwegian and Spanish consumers in association with a healthy and positive meal. Additionally, given the limited awareness of the origin of fish, transparency on the issue of farmed origin will be important in order to anticipate potential adverse communication

User centred evaluation of a recommendation based image browsing system

In this paper, we introduce a novel approach to recommend images by mining user interactions based on implicit feedback of user browsing. The underlying hypothesis is that the interaction implicitly indicates the interests of the users for meeting practical image retrieval tasks. The algorithm mines interaction data and also low-level content of the clicked images to choose diverse images by clustering heterogeneous features. A user-centred, task-oriented, comparative evaluation was undertaken to verify the validity of our approach where two versions of systems { one set up to enable diverse image recommendation { the other allowing browsing only { were compared. Use was made of the two systems by users in simulated work task situations and quantitative and qualitative data collected as indicators of recommendation results and the levels of user's satisfaction. The responses from the users indicate that they nd the more diverse recommendation highly useful

3D Object Class Detection in the Wild

Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations. Only recently, there has been a growing interest in revisiting the promise of computer vision from the early days: to precisely delineate the contents of a visual scene, object by object, in 3D. In this paper, we draw from recent advances in object detection and 2D-3D object lifting in order to design an object class detector that is particularly tailored towards 3D object class detection. Our 3D object class detection method consists of several stages gradually enriching the object detection output with object viewpoint, keypoints and 3D shape estimates. Following careful design, in each stage it constantly improves the performance and achieves state-ofthe-art performance in simultaneous 2D bounding box and viewpoint estimation on the challenging Pascal3D+ dataset