  Salient object detection is a prevalent computer vision task that has
applications ranging from abnormality detection to abnormality processing.
Context modelling is an important criterion in the domain of saliency
detection. A global context helps in determining the salient object in a given
image by contrasting away other objects in the global view of the scene.
However, the local context features detects the boundaries of the salient
object with higher accuracy in a given region. To incorporate the best of both
worlds, our proposed SaLite model uses both global and local contextual
features. It is an encoder-decoder based architecture in which the encoder uses
a lightweight SqueezeNet and decoder is modelled using convolution layers.
Modern deep based models entitled for saliency detection use a large number of
parameters, which is difficult to deploy on embedded systems. This paper
attempts to solve the above problem using SaLite which is a lighter process for
salient object detection without compromising on performance. Our approach is
extensively evaluated on three publicly available datasets namely DUTS,
MSRA10K, and SOC. Experimental results show that our proposed SaLite has
significant and consistent improvements over the state-of-the-art methods.
