  Foreground segmentation is an essential task in the field of image
understanding. Under unsupervised conditions, different images and instances
always have variable expressions, which make it difficult to achieve stable
segmentation performance based on fixed rules or single type of feature. In
order to solve this problem, the research proposes an unsupervised foreground
segmentation method based on semantic-apparent feature fusion (SAFF). Here, we
found that key regions of foreground object can be accurately responded via
semantic features, while apparent features (represented by saliency and edge)
provide richer detailed expression. To combine the advantages of the two type
of features, an encoding method for unary region features and binary context
features is established, which realizes a comprehensive description of the two
types of expressions. Then, a method for adaptive parameter learning is put
forward to calculate the most suitable feature weights and generate foreground
confidence score map. Furthermore, segmentation network is used to learn
foreground common features from different instances. By fusing semantic and
apparent features, as well as cascading the modules of intra-image adaptive
feature weight learning and inter-image common feature learning, the research
achieves performance that significantly exceeds baselines on the PASCAL VOC
2012 dataset.
