  Due to the availability of increasingly large amounts of visual data, there
is a growing need for tools that can help users find relevant images. While
existing tools can perform image retrieval based on similarity or metadata,
they fall short in scenarios that necessitate semantic reasoning about the
content of the image. This paper explores a new multi-modal image search
approach that allows users to conveniently specify and perform semantic image
search tasks. With our tool, PhotoScout, the user interactively provides
natural language descriptions, positive and negative examples, and object tags
to specify their search tasks. Under the hood, PhotoScout is powered by a
program synthesis engine that generates visual queries in a domain-specific
language and executes the synthesized program to retrieve the desired images.
In a study with 25 participants, we observed that PhotoScout allows users to
perform image retrieval tasks more accurately and with less manual effort.
