The symbolic, socio-economic and exchange value of Imperial German and National Socialist medals and badges, 1701 to the present

This thesis examines the manufacture, use, exchange (including gift exchange), collecting and commodification of German medals and badges from the early 18th century until the present-day, with particular attention being given to the symbols that were deployed by the National Socialist German Workers’ Party (NSDAP) between 1919 and 1945. It does so by focusing in particular on the construction of value through insignia, and how such badges and their symbolic and monetary value changed over time. In order to achieve this, the thesis adopts a chronological structure, which encompasses the creation of Prussia in 1701, the Napoleonic wars and the increased democratisation of military awards such as the Iron Cross during the Great War. The collapse of the Kaiserreich in 1918 was the major factor that led to the creation of the NSDAP under the eventual strangle-hold of Hitler, a fundamentally racist and anti-Semitic movement that continued the German tradition of awarding and wearing badges. The traditional symbols of Imperial Germany, such as the eagle, were then infused with the swastika, an emblem that was meant to signify anti-Semitism, thus creating a hybrid identity. This combination was then replicated en-masse, and eventually eclipsed all the symbols that had possessed symbolic significance in Germany’s past. After Hitler was appointed Chancellor in 1933, millions of medals and badges were produced in an effort to create a racially based “People’s Community”, but the steel and iron that were required for munitions eventually led to substitute materials being utilised and developed in order to manufacture millions of politically oriented badges. The Second World War unleashed Nazi terror across Europe, and the conscripts and volunteers who took part in this fight for living-space were rewarded with medals that were modelled on those that had been instituted during Imperial times. The colonial conquest and occupation of the East by the Wehrmacht, the Order Police and the Waffen-SS surpassed the brutality of former wars that finally culminated in the Holocaust, and some of these horrific crimes and the perpetrators of them were perversely rewarded with medals and badges. Despite Nazism being thoroughly discredited, many of the Allied soldiers who occupied Germany took part in the age-old practice of obtaining trophies of war, which reconfigured the meaning of Nazi badges as souvenirs, and began the process of their increased commodification on an emerging secondary collectors’ market. In order to analyse the dynamics of this market, a “basket” of badges is examined that enables a discussion of the role that aesthetics, scarcity and authenticity have in determining the price of the artefacts. In summary, this thesis demonstrates how the symbolic, socio-economic and exchange value of German military and political medals and badges has changed substantially over time, provides a stimulus for scholars to conduct research in this under-developed area, and encourages collectors to investigate the artefacts that they collect in a more historically contextualised manner.

The role of context in image annotation and recommendation

With the rise of smart phones, lifelogging devices (e.g. Google Glass) and popularity of image sharing websites (e.g. Flickr), users are capturing and sharing every aspect of their life online producing a wealth of visual content. Of these uploaded images, the majority are poorly annotated or exist in complete semantic isolation making the process of building retrieval systems difficult as one must firstly understand the meaning of an image in order to retrieve it. To alleviate this problem, many image sharing websites offer manual annotation tools which allow the user to “tag” their photos, however, these techniques are laborious and as a result have been poorly adopted; Sigurbjörnsson and van Zwol (2008) showed that 64% of images uploaded to Flickr are annotated with < 4 tags. Due to this, an entire body of research has focused on the automatic annotation of images (Hanbury, 2008; Smeulders et al., 2000; Zhang et al., 2012a) where one attempts to bridge the semantic gap between an image’s appearance and meaning e.g. the objects present. Despite two decades of research the semantic gap still largely exists and as a result automatic annotation models often offer unsatisfactory performance for industrial implementation. Further, these techniques can only annotate what they see, thus ignoring the “bigger picture” surrounding an image (e.g. its location, the event, the people present etc). Much work has therefore focused on building photo tag recommendation (PTR) methods which aid the user in the annotation process by suggesting tags related to those already present. These works have mainly focused on computing relationships between tags based on historical images e.g. that NY and timessquare co-exist in many images and are therefore highly correlated. However, tags are inherently noisy, sparse and ill-defined often resulting in poor PTR accuracy e.g. does NY refer to New York or New Year? This thesis proposes the exploitation of an image’s context which, unlike textual evidences, is always present, in order to alleviate this ambiguity in the tag recommendation process. Specifically we exploit the “what, who, where, when and how” of the image capture process in order to complement textual evidences in various photo tag recommendation and retrieval scenarios. In part II, we combine text, content-based (e.g. # of faces present) and contextual (e.g. day-of-the-week taken) signals for tag recommendation purposes, achieving up to a 75% improvement to precision@5 in comparison to a text-only TF-IDF baseline. We then consider external knowledge sources (i.e. Wikipedia & Twitter) as an alternative to (slower moving) Flickr in order to build recommendation models on, showing that similar accuracy could be achieved on these faster moving, yet entirely textual, datasets. In part II, we also highlight the merits of diversifying tag recommendation lists before discussing at length various problems with existing automatic image annotation and photo tag recommendation evaluation collections. In part III, we propose three new image retrieval scenarios, namely “visual event summarisation”, “image popularity prediction” and “lifelog summarisation”. In the first scenario, we attempt to produce a rank of relevant and diverse images for various news events by (i) removing irrelevant images such memes and visual duplicates (ii) before semantically clustering images based on the tweets in which they were originally posted. Using this approach, we were able to achieve over 50% precision for images in the top 5 ranks. In the second retrieval scenario, we show that by combining contextual and content-based features from images, we are able to predict if it will become “popular” (or not) with 74% accuracy, using an SVM classifier. Finally, in chapter 9 we employ blur detection and perceptual-hash clustering in order to remove noisy images from lifelogs, before combining visual and geo-temporal signals in order to capture a user’s “key moments” within their day. We believe that the results of this thesis show an important step towards building effective image retrieval models when there lacks sufficient textual content (i.e. a cold start).