Pigs, hogs and Aussie blokes: the emergence of the term 'six o'clock swill'

‘Six o’clock swill’ is one of the best known terms in Australian history, popularly associated with the drinking practices of a fifty-year period when pubs closed at six o’clock in most Australian states. Historians have tended to link the emergence of the’six o’clock swill’ to the introduction of early or six o’clock closing during the Great War. A closer analysis suggests it was not licensing law alone which impelled its emergence but historically specific conditions during World War II. Moreover, the term ‘six o’clock swill’ was no mere description of drinking practices; importantly it generated cultural politics particular to time and place.

Object matching using boundary descriptors

The problem of object recognition is of immense practical importance and potential, and the last decade has witnessed a number of breakthroughs in the state of the art. Most of the past object recognition work focuses on textured objects and local appearance descriptors extracted around salient points in an image. These methods fail in the matching of smooth, untextured objects for which salient point detection does not produce robust results. The recently proposed bag of boundaries (BoB) method is the first to directly address this problem. Since the texture of smooth objects is largely uninformative, BoB focuses on describing and matching objects based on their post-segmentation boundaries. Herein we address three major weaknesses of this work. The first of these is the uniform treatment of all boundary segments. Instead, we describe a method for detecting the locations and scales of salient boundary segments. Secondly, while the BoB method uses an image based elementary descriptor (HoGs + occupancy matrix), we propose a more compact descriptor based on the local profile of boundary normals’ directions. Lastly, we conduct a far more systematic evaluation, both of the bag of boundaries method and the method proposed here. Using a large public database, we demonstrate that our method exhibits greater robustness while at the same time achieving a major computational saving – object representation is extracted from an image in only 6% of the time needed to extract a bag of boundaries, and the storage requirement is similarly reduced to less than 8%.