Kaneko Ayaka, Komatsu Akiko, Itoh Takayuki, Wang Florence Ying
Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo, 1128610, Japan.
CSIRO, Julius Ave., North Ryde,0 NSW, 2113, Australia.
Vis Comput Ind Biomed Art. 2020 Feb 5;3(1):3. doi: 10.1186/s42492-019-0040-7.
Exploration of artworks is enjoyable but often time consuming. For example, it is not always easy to discover the favorite types of unknown painting works. It is not also always easy to explore unpopular painting works which looks similar to painting works created by famous artists. This paper presents a painting image browser which assists the explorative discovery of user-interested painting works. The presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting images. This study assumes a large number of painting images are provided where categorical information (e.g., names of artists, created year) is assigned to the images. The presented system firstly calculates the feature values of the images as a preprocessing step. Then the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical information. This mechanism enables users to explore favorite painting images or painting images that look similar to famous painting works. Our case study and user evaluation demonstrates the effectiveness of the presented image browser.
对艺术作品的探索是令人愉快的，但往往耗时较长。例如，发现未知绘画作品中用户喜欢的类型并非总是易事。探索那些看起来与著名艺术家创作的绘画作品相似的冷门绘画作品也并非总是轻而易举。本文提出了一种绘画图像浏览器，它有助于探索发现用户感兴趣的绘画作品。所提出的浏览器应用了一种新的多维数据可视化技术，该技术基于关联规则突出显示特定数值的特定范围，以提供线索来找到用户喜欢的绘画图像。本研究假设提供了大量绘画图像，且已为这些图像分配了分类信息（例如，艺术家姓名、创作年份）。所提出的系统首先将图像的特征值作为预处理步骤进行计算。然后，浏览器将多维特征值可视化为热图，并突出显示从特征值与分类信息之间的关系中发现的关联规则。这种机制使用户能够探索喜欢的绘画图像或与著名绘画作品相似的绘画图像。我们的案例研究和用户评估证明了所提出的图像浏览器的有效性。
