Thomas Methven

How Well Do Computational Features Perceptually Rank Textures? A Comparative Evaluation

Inspired by studies [4, 23, 40] which compared rankings obtained by search engines and human observers, in this paper we compare texture rankings derived by 51 sets of computational features against perceptual texture rankings obtained from a free-grouping experiment with 30 human observers, using a unify evaluation framework. Experimental results show that the MRSAR [37], VZNEIGHBORHOOD [62], LBPHF [2] and LBPBASIC [3] feature sets perform better than their counterparts. However, none of those feature sets are ideal. The best average G and M measures (measures of ranking accuracy from 0 to 1) [15, 5] obtained are 0.36 and 0.25 respectively. We suggest that this poor performance may be due to the small local neighborhood used to calculate higher-order features which cannot capture the long-range interactions that humans have been shown to exploit [14, 16, 49, 56].

A Picture Paints a Thousand Words but Can it Paint Just One

Imagery and language are often seen as serving different aspects of cognition, with cognitive styles theories proposing that people can be visual or verbal thinkers. Most feedback systems, however, only cater to verbal thinkers. To help rectify this, we have developed a novel method of crowd communication which appeals to those more visual people. Designers can ask a crowd to feedback on their designs using specially constructed image banks to discover the perceptual and emotional theme perceived by possible future customers. A major component of the method is a summarization process in which the crowds feedback, consisting of a mass of images, is presented to the designer as a digest of representative images. In this paper we describe an experiment showing that these image summaries are as effective as the full image selections at communicating terms. This means that designers can consume the new feedback confident that it represents a fair representation of the total image feedback from the crowd.

I Don't Think We've Met: Encouraging Collaboration via Topic-Based Search

We present PaperPilot1 (bit.ly/paperpilot) a new tool which performs smart collaborator search using research concepts automatically extracted from the CSCW domain, as characterized by 5,516 papers taken from four conferences in the area. PaperPilot infers how a paragraph of text (say an abstract or news article) relates to these research concepts and uses this information to retrieve the 100 most similar papers and identify the most relevant topic for each. These topics can be used both to obtain a quick overview of the papers and as an ice breaker for opening conversations with potential collaborators. To ensure the smart collaborator search is relevant to CSCW 2015 attendees, all accepted papers and authors will also be included.

Understanding Concept Maps: A Closer Look at How People Organise Ideas

Research into creating visualisations that organise ideas into concise concept maps often focuses on implicit mathematical and statistical theories which are built around algorithmic efficacy or visual complexity. Although there are multiple techniques which attempt to mathematically optimise this multi-dimensional problem, it is still unknown how to create concept maps that are immediately understandable to people. In this paper, we present an in-depth qualitative study observing the behaviour and discussing the strategy used by non-expert participants to create, interact, update and communicate a concept map that represents a collection of research ideas. Our results show non-expert individuals create concept maps differently to visualisation algorithms. We found that our participants prioritised narrative, landmarks, abstraction, clarity, and simplicity. Finally, we derive design recommendations from our results which we hope will inspire future algorithms that automatically create more usable and compelling concept maps better suited to the natural behaviours and needs of users.

What To Study In HCI? A Reflection Based On CHI and UK Research Data

HCI is a wide, varied, and complex field that covers a broad spectrum of research. We therefore believe that there is no simple answer to the question ‘what to study in HCI?’ To shed some light on it, however, we reflect on this question with the aid of data from past HCI conferences, present meta-analyses reports, and possible future research priorities. In our discussion, we argue that the current focus of HCI research is too focused on studying the usability of gadgets. Instead, we believe that researchers in the HCI field have the unique opportunity to combine fundamental research, usability design, and awareness of social issues to achieve real-world impact. As such, we suggest that researchers should aim their studies on human aspects that can solve various needs, problems, and societal challenges. Author