Ancient Coin Classification Using Graph Transduction Games

Recognizing the type of an ancient coin requires theoretical expertise and years of experience in the field of numismatics. Our goal in this work is automatizing this time consuming and demanding task by a visual classification framework. Specifically, we propose to model ancient coin image classification using Graph Transduction Games (GTG). GTG casts the classification problem as a non-cooperative game where the players (the coin images) decide their strategies (class labels) according to the choices made by the others, which results with a global consensus at the final labeling. Experiments are conducted on the only publicly available dataset which is composed of 180 images of 60 types of Roman coins. We demonstrate that our approach outperforms the literature work on the same dataset with the classification accuracy of 73.6% and 87.3% when there are one and two images per class in the training set, respectively

Aligning Figurative Paintings With Their Sources for Semantic Interpretation

This paper reports steps in probing the artistic methods of figurative painters through computational algorithms. We explore a comparative method that investigates the relation between the source of a painting, typically a photograph or an earlier painting, and the painting itself. A first crucial step in this process is to find the source and to crop, standardize and align it to the painting so that a comparison becomes possible. The next step is to apply different low-level algorithms to construct difference maps for color, edges, texture, brightness, etc. From this basis, various subsequent operations become possible to detect and compare features of the image, such as facial action units and the emotions they signify. This paper demonstrates a pipeline we have built and tested using paintings by a renowned contemporary painter Luc Tuymans. We focus in this paper particularly on the alignment process, on edge difference maps, and on the utility of the comparative method for bringing out the semantic significance of a painting

Compressively Sensed Image Recognition

Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudo-random measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases.Comment: 6 pages, submitted/accepted, EUVIP 201

Unsupervised Domain Adaptation using Graph Transduction Games

Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain. In this paper, we propose to cast this problem in a game-theoretic setting as a non-cooperative game and introduce a fully automatized iterative algorithm for UDA based on graph transduction games (GTG). The main advantages of this approach are its principled foundation, guaranteed termination of the iterative algorithms to a Nash equilibrium (which corresponds to a consistent labeling condition) and soft labels quantifying the uncertainty of the label assignment process. We also investigate the beneficial effect of using pseudo-labels from linear classifiers to initialize the iterative process. The performance of the resulting methods is assessed on publicly available object recognition benchmark datasets involving both shallow and deep features. Results of experiments demonstrate the suitability of the proposed game-theoretic approach for solving UDA tasks.Comment: Oral IJCNN 201

Migration and security : history, practice, and theory

Cataloged from PDF version of article.Receiving states viewed international migration as a means of economic development well until late 20th century. Since then policy makers around the world have increasingly associated migration to security and sought to meet this ‘threat’ through ‘control’. In the 21st century, the significance of international migration increased further as migration flows increased and took on new forms affecting the world as a whole. This thesis looks at the emergence of migration as a security issue in the practices of world actors within a historical and contextual framework and highlights the politics of associating migration with security. In doing so, it does not take as pre-given a relationship between migration and security. Two interrelated arguments are made. First, migration’s association with security has been context-bound. Second, whether migration is a security issue or not changes according to actors (in the policy and scholarly worlds). Critical approaches to security, focusing on the role of state and societal actors in associating migration to security, and stressing security of not only states but also individuals, offer a fuller account of migration. Whereas objectivist approaches to security take migration as a ‘real’ threat, and fundamentally in relation to state security and national interest.Aslan, Nazlı SinemM.S

Development of an Entrepreneurial Small Business’ (Abler by Robomedika’s) Strategic Plan by Shortened Systematic Strategic Planning (SSP)—Case Study

As many sources exhort managers to “think strategically”, only a few addresses how to make this happen. Shortened Systematic Strategic Planning (SSP) consists of a pattern of step-wise procedure for straight-forward planning, and the fundamentals involved in any strategic planning project. The use of shortened SSP is more suitable for the development of strategic plans for small- and medium-size businesses. SSP has been applied to and tested on different businesses’ subject issue and has been generated by the composition of the cause-and-effect relations of them. The intention here is to provide a new perspective and benefit for the strategic planners by introducing this new systematic methodology and demonstrating its implementation on an entrepreneurial and new business called Abler. Accordingly, let shortened version of SSP easily understood and universally applied to any small- and medium-size businesses. You are guided how to identify in what circumstances you might use its specific tools and how to target them directly at achieving effective results. The data that are used in this case are fictitious and only help for this study. Though, the given case does not cover all the steps of a typical SSP and use all the recommended techniques, it still reflects the basics

Transductive Visual Verb Sense Disambiguation

Verb Sense Disambiguation is a well-known task in NLP, the aim is to find the correct sense of a verb in a sentence. Recently, this problem has been extended in a multimodal scenario, by exploiting both textual and visual features of ambiguous verbs leading to a new problem, the Visual Verb Sense Disambiguation (VVSD). Here, the sense of a verb is assigned considering the content of an image paired with it rather than a sentence in which the verb appears. Annotating a dataset for this task is more complex than textual disambiguation, because assigning the correct sense to a pair of requires both non-trivial linguistic and visual skills. In this work, differently from the literature, the VVSD task will be performed in a transductive semi-supervised learning (SSL) setting, in which only a small amount of labeled information is required, reducing tremendously the need for annotated data. The disambiguation process is based on a graph-based label propagation method which takes into account mono or multimodal representations for pairs. Experiments have been carried out on the recently published dataset VerSe, the only available dataset for this task. The achieved results outperform the current state-of-the-art by a large margin while using only a small fraction of labeled samples per sens