  In this paper, we propose an image matting framework called Salient Image
Matting to estimate the per-pixel opacity value of the most salient foreground
in an image. To deal with a large amount of semantic diversity in images, a
trimap is conventionally required as it provides important guidance about
object semantics to the matting process. However, creating a good trimap is
often expensive and timeconsuming. The SIM framework simultaneously deals with
the challenge of learning a wide range of semantics and salient object types in
a fully automatic and an end to end manner. Specifically, our framework is able
to produce accurate alpha mattes for a wide range of foreground objects and
cases where the foreground class, such as human, appears in a very different
context than the train data directly from an RGB input. This is done by
employing a salient object detection model to produce a trimap of the most
salient object in the image in order to guide the matting model about
higher-level object semantics. Our framework leverages large amounts of coarse
annotations coupled with a heuristic trimap generation scheme to train the
trimap prediction network so it can produce trimaps for arbitrary foregrounds.
Moreover, we introduce a multi-scale fusion architecture for the task of
matting to better capture finer, low-level opacity semantics. With high-level
guidance provided by the trimap network, our framework requires only a fraction
of expensive matting data as compared to other automatic methods while being
able to produce alpha mattes for a diverse range of inputs. We demonstrate our
framework on a range of diverse images and experimental results show our
framework compares favourably against state of art matting methods without the
need for a trimap
