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.

中文翻译：

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绘画图像浏览器应用了基于关联规则的多维数据可视化技术。

对艺术品的探索是令人愉快的，但通常很耗时。例如，发现未知绘画作品的喜爱类型并不总是那么容易。探索与著名艺术家创作的绘画不相似的不受欢迎的绘画作品也不总是那么容易。本文提出了一种绘画图像浏览器，该浏览器有助于探索性地发现用户感兴趣的绘画作品。所展示的浏览器应用了一种新的多维数据可视化技术，该技术可以根据关联规则突出显示特定数值的特定范围，从而为寻找喜欢的绘画图像提供提示。这项研究假设提供了大量绘画图像，其中将类别信息（例如，艺术家的姓名，创作的年份）分配给了这些图像。所提出的系统首先计算图像的特征值作为预处理步骤。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和分类信息之间的关系中发现的关联规则。该机制使用户能够浏览喜欢的绘画图像或看起来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供图像浏览器的有效性。该机制使用户能够浏览喜欢的绘画图像或看起来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供图像浏览器的有效性。该机制使用户能够浏览喜欢的绘画图像或看起来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供图像浏览器的有效性。