  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.
