What is and What is not a Salient Object?
Learning Salient Object Detector by Ensembling Linear Exemplar Regressors
Abstract
Finding what is and what is not a salient object can be
helpful in developing better features and models in salient
object detection (SOD). In this paper, we investigate the images that are selected and discarded in constructing a new
SOD dataset and find that many similar candidates, complex shape and low objectness are three main attributes of
many non-salient objects. Moreover, objects may have diversified attributes that make them salient. As a result, we
propose a novel salient object detector by ensembling linear exemplar regressors. We first select reliable foreground
and background seeds using the boundary prior and then
adopt locally linear embedding (LLE) to conduct manifoldpreserving foregroundness propagation. In this manner, a
foregroundness map can be generated to roughly pop-out
salient objects and suppress non-salient ones with many
similar candidates. Moreover, we extract the shape, foregroundness and attention descriptors to characterize the extracted object proposals, and a linear exemplar regressor is
trained to encode how to detect salient proposals in a specific image. Finally, various linear exemplar regressors are
ensembled to form a single detector that adapts to various
scenarios. Extensive experimental results on 5 dataset and
the new SOD dataset show that our approach outperforms
9 state-of-art methods