Doktorarbeiten and Film in Germany : 1910-1945 ; an overview and catalogue

Die folgende Bibliographie erschien zunächst in der Film Theory: Bibliographic Information and Newsletter, nos. 14-17, 1987, pp. 131-161 (= G227-G257

Image Understanding by Socializing the Semantic Gap

Several technological developments like the Internet, mobile devices and Social Networks have spurred the sharing of images in unprecedented volumes, making tagging and commenting a common habit. Despite the recent progress in image analysis, the problem of Semantic Gap still hinders machines in fully understand the rich semantic of a shared photo. In this book, we tackle this problem by exploiting social network contributions. A comprehensive treatise of three linked problems on image annotation is presented, with a novel experimental protocol used to test eleven state-of-the-art methods. Three novel approaches to annotate, under stand the sentiment and predict the popularity of an image are presented. We conclude with the many challenges and opportunities ahead for the multimedia community

Contextualizing the apparatus : film in the turn-of-the-century Sears, Roebuck & Co.’s Consumers guide

Diffusé avec l’accord des Éditions Amsterdam University Press, détentrices des droits d’auteur sur ce texte.The Sears, Roebuck & CO. 1898 Consumers Guide Published by America and Canada’s largest mail order company, the 10.8 x 8.5 inches (27.4 x 21.6 cm), 1120-page catalog was filled with illustrations, descriptions, and testimonials regarding every possible commodity, including motion-picture-related items. Several million copies per year found their way to farms and small towns across the continent. The Consumers Guide offered projectors and films for sale, itemizing the medium’s technological requirements, its business models, and programming possibilities for the general public. It provides a highly detailed documentation of what the turn-of-the-century public could be expected to know about the film medium shortly after its birth. Theoretical Framing : A catalog can be an apparatus of sorts, as a causal agent and source of documentation and evidence. The 1898 Sears Consumers Guide helped to position the medium of film in the imagination of millions of readers, locating it as a technology, business opportunity, and source of information and entertainment. From a 21st-century perspective, it offers evidence regarding the intricate relationship of technology and text, of the material conditions facing those who would use the medium, and of the period’s cross-media endeavors. The Consumers Guide is an apparatus for understanding the horizon of expectations that greeted the new medium of motion pictures

Localization of JPEG double compression through multi-domain convolutional neural networks

When an attacker wants to falsify an image, in most of cases she/he will perform a JPEG recompression. Different techniques have been developed based on diverse theoretical assumptions but very effective solutions have not been developed yet. Recently, machine learning based approaches have been started to appear in the field of image forensics to solve diverse tasks such as acquisition source identification and forgery detection. In this last case, the aim ahead would be to get a trained neural network able, given a to-be-checked image, to reliably localize the forged areas. With this in mind, our paper proposes a step forward in this direction by analyzing how a single or double JPEG compression can be revealed and localized using convolutional neural networks (CNNs). Different kinds of input to the CNN have been taken into consideration, and various experiments have been carried out trying also to evidence potential issues to be further investigated.Comment: Accepted to CVPRW 2017, Workshop on Media Forensic

Applications of Powerset Operators, Especially to Matroids

Let $\mathcal{V}$ denote a vector space over an arbitrary field with an inner product. For any collection $\mathcal{S}$ of vectors from $\mathcal{V}$ the collection of all vectors orthogonal to each vector in $\mathcal{S}$ is a subspace, denoted as $\mathcal{S}^{\perp_v}$ and called the \textit{orthogonal complement} of $\mathcal{S}$. One of the fundamental theorems of vector space theory states that, $(\mathcal{S}^{\perp_v})^{\perp_v}$ is the subspace \textit{spanned} by $\mathcal{S}$. Thus the ``spanning\u27\u27 operator on the subsets of a vector space is the square of the ``orthogonal complement\u27\u27 operator. In matroid theory, the orthogonal complement of a matroid $M$ is also well-defined and similarly results in another matroid. Although this new matroid is more commonly referred to as the `dual matroid\u27, denoted as $M^*$, and typically formed using a very different approach. There is an interesting relation between the circuits of a matroid $M$ and the cocircuits of $M$ (the circuits of its dual matroid $M^*$) which aligns much more closely to the orthogonal complement of a vector space. We expand on this relation to define a powerset operator: $(\phantom{S})^*$. Given $\mathcal{S} \subseteq \mathcal{P}(E)$, we denote $\mathcal{S}^*$ to be the minimal sets of $\{X \subseteq E \colon X \text{ is nonempty, } |X \cap A| \neq 1 \text{ for each } A \in \mathcal{S}\}$. We call this powerset operator the \textbf{circuit duality operator}. Unlike the vector space orthogonal complement operator, this circuit duality operator may not behave as nicely when applied to collections that do not correspond to a matroid. This thesis is an investigation into the development of tools and additional operators to help understand the collections of sets that result in a matroid under one or more applications of the circuit duality operator

Am I Done? Predicting Action Progress in Videos

In this paper we deal with the problem of predicting action progress in videos. We argue that this is an extremely important task since it can be valuable for a wide range of interaction applications. To this end we introduce a novel approach, named ProgressNet, capable of predicting when an action takes place in a video, where it is located within the frames, and how far it has progressed during its execution. To provide a general definition of action progress, we ground our work in the linguistics literature, borrowing terms and concepts to understand which actions can be the subject of progress estimation. As a result, we define a categorization of actions and their phases. Motivated by the recent success obtained from the interaction of Convolutional and Recurrent Neural Networks, our model is based on a combination of the Faster R-CNN framework, to make frame-wise predictions, and LSTM networks, to estimate action progress through time. After introducing two evaluation protocols for the task at hand, we demonstrate the capability of our model to effectively predict action progress on the UCF-101 and J-HMDB datasets