We propose the Mood Board Composer (MBC), which supports concept designers in retrieving and composing images on a 2-D concept space to communicate design concepts. The MBC allows users to iterate adaptive image retrievals intuitively. Our new contribution to the mood board tool is to adapt the query vector for the next iteration according to the user's rearrangement of images on the 2-D space. The algorithm emphasizes the meaning of the labels on the x- and y-axes by calculating the mean vector of the images on the mood board multiplied by the weights assigned to each cell of the 3 x 3 grid. The next image search is performed by obtaining the most similar words from the mean vector thus obtained and using them as a new query. In addition to the algorithm described above, we conducted the participant experiment with two other interaction algorithms to compare. The first allows users to delete unwanted images and go on to the next searches. The second utilizes the semantic labels on each image, on which users can provide negative feedback for query modification for the next searches. Although we did not observe significant differences among the three proposed algorithms, our experiment with 420 cases of mood board creation confirmed the effectiveness of adaptive iterations by the Creativity Support Index (CSI) score.

翻译：我们提出了情绪板编辑器（Mood Board Composer, MBC），该工具支持概念设计师在二维概念空间中检索和编排图像，以传达设计概念。MBC允许用户直观地迭代自适应图像检索。我们对情绪板工具的新贡献在于根据用户在二维空间中对图像的重新排列来调整下一轮迭代的查询向量。该算法通过计算情绪板上图像的均值向量（该向量乘以3×3网格中每个单元格的权重）来强化x轴和y轴上标签的含义。下一轮图像搜索通过从所得均值向量中获取最相似的词语并将其作为新查询来实现。除上述算法外，我们开展了包含另外两种交互算法的参与者实验进行比较：第一种允许用户删除不想要的图像并进行后续搜索；第二种利用每张图像上的语义标签，用户可对其提供负反馈以修改后续查询。虽然我们未观察到三种提议算法之间的显著差异，但基于420个情绪板创建案例的实验通过创造力支持指数（CSI）得分证实了自适应迭代的有效性。