Learning visual representations of style

Learning Visual Representations of Style Door Nanne van Noord De stijl van een kunstenaar is zichtbaar in zijn/haar werk, onafhankelijk van de vorm of het onderwerp van een kunstwerk kunnen kunstexperts deze stijl herkennen. Of het nu om een landschap of een portret gaat, het connaisseurschap van kunstexperts stelt hen in staat om de stijl van de kunstenaar te herkennen. Het vertalen van dit vermogen tot connaisseurschap naar een computer, zodat de computer in staat is om de stijl van een kunstenaar te herkennen, en om kunstwerken te (re)produceren in de stijl van de kunstenaar, staat centraal in dit onderzoek. Voor visuele analyseren van kunstwerken maken computers gebruik van beeldverwerkingstechnieken. Traditioneel gesproken bestaan deze technieken uit door computerwetenschappers ontwikkelde algoritmes die vooraf gedefinieerde visuele kernmerken kunnen herkennen. Omdat deze kenmerken zijn ontwikkelt voor de analyse van de inhoud van foto’s zijn ze beperkt toepasbaar voor de analyse van de stijl van visuele kunst. Daarnaast is er ook geen definitief antwoord welke visuele kenmerken indicatief zijn voor stijl. Om deze beperkingen te overkomen maken we in dit onderzoek gebruik van Deep Learning, een methodologie die het beeldverwerking onderzoeksveld in de laatste jaren enorm heeft gerevolutionaliseerd. De kracht van Deep Learning komt voort uit het zelflerende vermogen, in plaats van dat we afhankelijk zijn van vooraf gedefinieerde kenmerken, kan de computer zelf leren wat de juiste kenmerken zijn. In dit onderzoek hebben we algoritmes ontwikkelt met het doel om het voor de computer mogelijk te maken om 1) zelf te leren om de stijl van een kunstenaar te herkennen, en 2) nieuwe afbeeldingen te genereren in de stijl van een kunstenaar. Op basis van het in het proefschrift gepresenteerde werk kunnen we concluderen dat de computer inderdaad in staat is om te leren om de stijl van een kunstenaar te herkennen, ook in een uitdagende setting met duizenden kunstwerken en enkele honderden kunstenaars. Daarnaast kunnen we concluderen dat het mogelijk is om, op basis van bestaande kunstwerken, nieuwe kunstwerken te generen in de stijl van de kunstenaar. Namelijk, een kleurloze afbeeldingen van een kunstwerk kan ingekleurd worden in de stijl van de kunstenaar, en wanneer er delen missen uit een kunstwerk is het mogelijk om deze missende stukken in te vullen (te retoucheren). Alhoewel we nog niet in staat zijn om volledig nieuwe kunstwerken te generen, is dit onderzoek een grote stap in die richting. Bovendien zijn de in dit onderzoek ontwikkelde technieken en methodes veelbelovend als digitale middelen ter ondersteuning van kunstexperts en restauratoren

Rethinking auto-colourisation of natural Images in the context of deep learning

Auto-colourisation is the ill-posed problem of creating a plausible full-colour image from a grey-scale prior. The current state of the art utilises image-to-image Generative Adversarial Networks (GANs). The standard method for training colourisation is reformulating RGB images into a luminance prior and two-channel chrominance supervisory signal. However, progress in auto-colourisation is inherently limited by multiple prerequisite dilemmas, where unsolved problems are mutual prerequisites. This thesis advances the field of colourisation on three fronts: architecture, measures, and data. Changes are recommended to common GAN colourisation architectures. Firstly, removing batch normalisation from the discriminator to allow the discriminator to learn the primary statistics of plausible colour images. Secondly, eliminating the direct L1 loss on the generator as L1 will limit the discovery of the plausible colour manifold. The lack of an objective measure of plausible colourisation necessitates resource-intensive human evaluation and repurposed objective measures from other fields. There is no consensus on the best objective measure due to a knowledge gap regarding how well objective measures model the mean human opinion of plausible colourisation. An extensible data set of human-evaluated colourisations, the Human Evaluated Colourisation Dataset (HECD) is presented. The results from this dataset are compared to the commonly-used objective measures and uncover a poor correlation between the objective measures and mean human opinion. The HECD can assess the future appropriateness of proposed objective measures. An interactive tool supplied with the HECD allows for a first exploration of the space of plausible colourisation. Finally, it will be shown that the luminance channel is not representative of the legacy black-and-white images that will be presented to models when deployed; This leads to out-of-distribution errors in all three channels of the final colour image. A novel technique is proposed to simulate priors that match any black-and-white media for which the spectral response is known

Difficult heritage and children’s summer camps of Fascist Italy

The Fascist regime helped itself to a diverse collection of children’s institutions, including summer camps, or colonie, which it put its stamp of ownership on, expanded, and adapted into a tool for indoctrination of youth under the guise of benevolent welfare. This research focuses on the legacy of the colonie built during the years of the Fascist regime whose current situations include demolition, abandonment, ruination, continuing use, and unfinished and successful renovations. The research explores how these spaces have fared over the years since they were established, and speculates as to what the future may hold for them as the problematic and ambiguous heritage of a totalitarian regime. The study is conducted through investigations into their past, present circumstances and speculative futures. Research methods include studies of primary and secondary texts, walking, photographic practice, and appropriation and reuse of archival and other imagery. The research is framed within concepts of heterotopia and difficult heritage, which it uses to expose ambiguities in representations of the colonie system, alongside an intrinsic ‘fuzziness’ in the Fascist regime’s ideology. The practical outcome of the research and practice comprises a set of photographic images and representations in the form of a photobook, in which ambiguities and contradictions are revealed through juxtapositions and sequences of images and the spaces between them. A nuanced approach allowed for understandings of the regime’s attitude towards the health and indoctrination of youth to emerge. The research concludes that consequences of Fascism’s appropriation of the colonie system are complex and multifaceted. Scholars acknowledge that further research is needed, and this study makes a contribution towards situating the architecture and memory of former Fascist colonia as heritage worth preserving, irrespective of inherent difficulties

Understanding and advancing PDE-based image compression

This thesis is dedicated to image compression with partial differential equations (PDEs). PDE-based codecs store only a small amount of image points and propagate their information into the unknown image areas during the decompression step. For certain classes of images, PDE-based compression can already outperform the current quasi-standard, JPEG2000. However, the reasons for this success are not yet fully understood, and PDE-based compression is still in a proof-of-concept stage. With a probabilistic justification for anisotropic diffusion, we contribute to a deeper insight into design principles for PDE-based codecs. Moreover, by analysing the interaction between efficient storage methods and image reconstruction with diffusion, we can rank PDEs according to their practical value in compression. Based on these observations, we advance PDE-based compression towards practical viability: First, we present a new hybrid codec that combines PDE- and patch-based interpolation to deal with highly textured images. Furthermore, a new video player demonstrates the real-time capacities of PDE-based image interpolation and a new region of interest coding algorithm represents important image areas with high accuracy. Finally, we propose a new framework for diffusion-based image colourisation that we use to build an efficient codec for colour images. Experiments on real world image databases show that our new method is qualitatively competitive to current state-of-the-art codecs.Diese Dissertation ist der Bildkompression mit partiellen Differentialgleichungen (PDEs, partial differential equations) gewidmet. PDE-Codecs speichern nur einen geringen Anteil aller Bildpunkte und transportieren deren Information in fehlende Bildregionen. In einigen Fällen kann PDE-basierte Kompression den aktuellen Quasi-Standard, JPEG2000, bereits schlagen. Allerdings sind die Gründe für diesen Erfolg noch nicht vollständig erforscht, und PDE-basierte Kompression befindet sich derzeit noch im Anfangsstadium. Wir tragen durch eine probabilistische Rechtfertigung anisotroper Diffusion zu einem tieferen Verständnis PDE-basierten Codec-Designs bei. Eine Analyse der Interaktion zwischen effizienten Speicherverfahren und Bildrekonstruktion erlaubt es uns, PDEs nach ihrem Nutzen für die Kompression zu beurteilen. Anhand dieser Einsichten entwickeln wir PDE-basierte Kompression hinsichtlich ihrer praktischen Nutzbarkeit weiter: Wir stellen einen Hybrid-Codec für hochtexturierte Bilder vor, der umgebungsbasierte Interpolation mit PDEs kombiniert. Ein neuer Video-Dekodierer demonstriert die Echtzeitfähigkeit PDE-basierter Interpolation und eine Region-of-Interest-Methode erlaubt es, wichtige Bildbereiche mit hoher Genauigkeit zu speichern. Schlussendlich stellen wir ein neues diffusionsbasiertes Kolorierungsverfahren vor, welches uns effiziente Kompression von Farbbildern ermöglicht. Experimente auf Realwelt-Bilddatenbanken zeigen die Konkurrenzfähigkeit dieses Verfahrens auf

Misdirect movies

Misdirect Movies is a curated touring exhibition exploring new possibilities of collage, employing material gleaned from cinema. With access to digital formats, artists are now able to appropriate films to create different and innovative approaches to collage. This builds upon research disseminated in artworks such as The Jump and Frames and the curated exhibition, Unspooling: Artists & Cinema The selected artists explore these ideas in diverse ways to work with narrative through different media. The exhibition will be supplemented by a catalogue, new artwork commissions, a series of artist/curator talks, film screenings, workshops and a website. The idea of the exhibition is to make us look anew at the familiarity of artist's use of collage, moving image and the cinema space. The exhibition includes work by the curators, alongside five artists from the UK, Germany and USA- Elizabeth McAlpine, Dave Griffiths, Cathy Lomax, Rosa Barba and David Reed. The selected artists work across different mediums and have a sustained engagement with the subject of the exhibition. There will be three new commissions launching at touring venues from the selected artists. The exhibition tours from Royal Standard, Liverpool (16-31 March 2013) and tours to Standpoint Gallery, London (5 July- 17 August 2013), Greyfriars, Lincoln (4-26 October 2013) and Meter Room, Coventry (8 November - 1 December 2013). The catalogue is published by Cornerhouse Publications and feature essays by Andrew Bracey, Dr. John Rimmer, Dr. Jaimie Baron, Dr. Maria Walsh and an interview between Dr. Sam George and Sir Christopher Frayling. The catalogue essays reflect the interdisciplinary nature of the exhibition's curatorial focus and feature contributions from visual arts, English literature and film studies backgrounds. The research is further disseminated by talks, critical essays on the website and introduced screenings of artist's films

Understanding and advancing PDE-based image compression

This thesis is dedicated to image compression with partial differential equations (PDEs). PDE-based codecs store only a small amount of image points and propagate their information into the unknown image areas during the decompression step. For certain classes of images, PDE-based compression can already outperform the current quasi-standard, JPEG2000. However, the reasons for this success are not yet fully understood, and PDE-based compression is still in a proof-of-concept stage. With a probabilistic justification for anisotropic diffusion, we contribute to a deeper insight into design principles for PDE-based codecs. Moreover, by analysing the interaction between efficient storage methods and image reconstruction with diffusion, we can rank PDEs according to their practical value in compression. Based on these observations, we advance PDE-based compression towards practical viability: First, we present a new hybrid codec that combines PDE- and patch-based interpolation to deal with highly textured images. Furthermore, a new video player demonstrates the real-time capacities of PDE-based image interpolation and a new region of interest coding algorithm represents important image areas with high accuracy. Finally, we propose a new framework for diffusion-based image colourisation that we use to build an efficient codec for colour images. Experiments on real world image databases show that our new method is qualitatively competitive to current state-of-the-art codecs.Diese Dissertation ist der Bildkompression mit partiellen Differentialgleichungen (PDEs, partial differential equations) gewidmet. PDE-Codecs speichern nur einen geringen Anteil aller Bildpunkte und transportieren deren Information in fehlende Bildregionen. In einigen Fällen kann PDE-basierte Kompression den aktuellen Quasi-Standard, JPEG2000, bereits schlagen. Allerdings sind die Gründe für diesen Erfolg noch nicht vollständig erforscht, und PDE-basierte Kompression befindet sich derzeit noch im Anfangsstadium. Wir tragen durch eine probabilistische Rechtfertigung anisotroper Diffusion zu einem tieferen Verständnis PDE-basierten Codec-Designs bei. Eine Analyse der Interaktion zwischen effizienten Speicherverfahren und Bildrekonstruktion erlaubt es uns, PDEs nach ihrem Nutzen für die Kompression zu beurteilen. Anhand dieser Einsichten entwickeln wir PDE-basierte Kompression hinsichtlich ihrer praktischen Nutzbarkeit weiter: Wir stellen einen Hybrid-Codec für hochtexturierte Bilder vor, der umgebungsbasierte Interpolation mit PDEs kombiniert. Ein neuer Video-Dekodierer demonstriert die Echtzeitfähigkeit PDE-basierter Interpolation und eine Region-of-Interest-Methode erlaubt es, wichtige Bildbereiche mit hoher Genauigkeit zu speichern. Schlussendlich stellen wir ein neues diffusionsbasiertes Kolorierungsverfahren vor, welches uns effiziente Kompression von Farbbildern ermöglicht. Experimente auf Realwelt-Bilddatenbanken zeigen die Konkurrenzfähigkeit dieses Verfahrens auf

Delving Deep into the Sketch and Photo Relation

"Sketches drawn by humans can play a similar role to photos in terms of conveying shape, posture as well as fine-grained information, and this fact has stimulated one line of cross-domain research that is related to sketch and photo, including sketch-based photo synthesis and retrieval. In this thesis, we aim to further investigate the relationship between sketch and photo. More specifically, we study certain under- explored traits in this relationship, and propose novel applications to reinforce the understanding of sketch and photo relation.Our exploration starts with the problem of sketch-based photo synthesis, where the unique trait of non-rigid alignment between sketch and photo is overlooked in existing research. We then carry on with our investigation from a new angle to study whether sketch can facilitate photo classifier generation. Building upon this, we continue to explore how sketch and photo are linked together on a more fine-grained level by tackling with the sketch-based photo segmenter prediction. Furthermore, we address the data scarcity issue identified in nearly all sketch-photo-related applications by examining their inherent correlation in the semantic aspect using sketch-based image retrieval (SBIR) as a test-bed. In general, we make four main contributions to the research on relationship between sketch and photo.Firstly, to mitigate the effect of deformation in sketch-based photo synthesis, we introduce the spatial transformer network to our image-image regression framework, which subtly deals with non-rigid alignment between the sketches and photos. The qualitative and quantitative experiments consistently reveal the superior quality of our synthesised photos over those generated by existing approaches.Secondly, sketch-based photo classifier generation is achieved with a novel model regression network, which maps the sketch to the parameters of photo classification model. It is shown that our model regression network is able to generalise across categories and photo classifiers for novel classes not involved in training are just a sketch away. Comprehensive experiments illustrate the promising performance of the generated binary and multi-class photo classifiers, and demonstrate that sketches can also be employed to enhance the granularity of existing photo classifiers.Thirdly, to achieve the goal of sketch-based photo segmentation, we propose a photo segmentation model generation algorithm that predicts the weights of a deep photo segmentation network according to the input sketch. The results confirm that one single sketch is the only prerequisite for unseen category photo segmentation, and the segmentation performance can be further improved by utilising sketch that is aligned with the object to be segmented in shape and position.Finally, we present an unsupervised representation learning framework for SBIR, the purpose of which is to eliminate the barrier imposed by data annotation scarcity. Prototype and memory bank reinforced joint distribution optimal transport is integrated into the unsupervised representation learning framework, so that the mapping between the sketches and photos could be automatically detected to learn a semantically meaningful yet domain-agnostic feature space. Extensive experiments and feature visualisation validate the efficacy of our proposed algorithm.