On the readability of leaders in boundary labeling

Abstract

External labeling deals with annotating features in images with labels that are placed outside of the image and are connected by curves (so-called leaders) to the corresponding features. While external labeling has been extensively investigated from a perspective of automatization, the research on its readability has been neglected. In this article, we present the first formal user study on the readability of leader types in boundary labeling, a special variant of external labeling that considers rectangular image contours. We consider the four most studied leader types (straight, L-shaped, diagonal, and S-shaped) with respect to their performance, that is, whether and how fast a viewer can assign a feature to its label and vice versa. We give a detailed analysis of the results regarding the readability of the four models and discuss their aesthetic qualities based on the users’ preference judgments and interviews. As a consequence of our experiment, we can generally recommend L-shaped leaders as the best compromise between measured task performance and subjective preference ratings, while straight and diagonal leaders received mixed ratings in the two measures. S-shaped leaders are generally not recommended from a practical point of view.

External Labeling Techniques: A Taxonomy and Survey

“…However, their approaches have asymptotically high running times, e.g., for the one-sided case they obtained a running time of O(n 11 ) and for the two-sided case they obtained a running time of O(n 27 ) when minimizing the total leader length. Barth et al [BGNN18] used an integer linear programming formulation to create one-sided boundary labelings with straight-line leaders (Section 5.2.5). They used these labelings in a user study on the readability of different leader types.…”

“…Straight-line leaders are the simplest way to establish the visual association between features and their external labels. Further, they can be easily traced by the reader [BGNN18]. We first give an overview on the current research (Section 4.1.1) and then discuss the individual papers concerning their particular contributions (Section 4.1.2).…”

Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols

“…This style of leader line was chosen as opposed to orthogonal leaders (i.e., leaders comprising one horizontal and one vertical segment). While in general, orthogonal leaders have been observed to have great legibility [6], we have found that diagonal lines stand out better amidst the clutter of other marks in an equation (M6).…”

“…We conduct a post-hoc analysis of pairwise contrasts to quantify the differences in mean effect of interface on y under the GLMM (and controlling for question). Two-sided t-tests for pairwise contrasts are computed using the emmeans R package, 6 and results are shown in Table 1.…”

“…We use the logit link д(p) = log(p/(1 −p)) for y = Correct, which is treated as a Bernoulli variable 5. The F-test is not applicable when y ∼ Bernoulli, so we performed the similar, but slightly less conservative, likelihood ratio test for y =Correct[49] 6. Because the GLMM for y =Correct was fit using a logit link, direct testing of pairwise contrasts ŷi − ŷj = P r (Correct = 1|i) − P r (Correct = 0|j) is not possible.…”