  In this paper, we propose a novel deep neural network framework embedded with
low-level features (LCNN) for salient object detection in complex images. We
utilise the advantage of convolutional neural networks to automatically learn
the high-level features that capture the structured information and semantic
context in the image. In order to better adapt a CNN model into the saliency
task, we redesign the network architecture based on the small-scale datasets.
Several low-level features are extracted, which can effectively capture
contrast and spatial information in the salient regions, and incorporated to
compensate with the learned high-level features at the output of the last fully
connected layer. The concatenated feature vector is further fed into a
hinge-loss SVM detector in a joint discriminative learning manner and the final
saliency score of each region within the bounding box is obtained by the linear
combination of the detector's weights. Experiments on three challenging
benchmark (MSRA-5000, PASCAL-S, ECCSD) demonstrate our algorithm to be
effective and superior than most low-level oriented state-of-the-arts in terms
of P-R curves, F-measure and mean absolute errors.
