Weakly Supervised Domain-Specific Color Naming Based on Attention

The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains.Comment: Accepted at ICPR201

Normalized Information Distance

The normalized information distance is a universal distance measure for objects of all kinds. It is based on Kolmogorov complexity and thus uncomputable, but there are ways to utilize it. First, compression algorithms can be used to approximate the Kolmogorov complexity if the objects have a string representation. Second, for names and abstract concepts, page count statistics from the World Wide Web can be used. These practical realizations of the normalized information distance can then be applied to machine learning tasks, expecially clustering, to perform feature-free and parameter-free data mining. This chapter discusses the theoretical foundations of the normalized information distance and both practical realizations. It presents numerous examples of successful real-world applications based on these distance measures, ranging from bioinformatics to music clustering to machine translation.Comment: 33 pages, 12 figures, pdf, in: Normalized information distance, in: Information Theory and Statistical Learning, Eds. M. Dehmer, F. Emmert-Streib, Springer-Verlag, New-York, To appea

Designing Familiar Open Surfaces

While participatory design makes end-users part of the design process, we might also want the resulting system to be open for interpretation, appropriation and change over time to reflect its usage. But how can we design for appropriation? We need to strike a good balance between making the user an active co-constructor of system functionality versus making a too strong, interpretative design that does it all for the user thereby inhibiting their own creative use of the system. Through revisiting five systems in which appropriation has happened both within and outside the intended use, we are going to show how it can be possible to design with open surfaces. These open surfaces have to be such that users can fill them with their own interpretation and content, they should be familiar to the user, resonating with their real world practice and understanding, thereby shaping its use