Exemplar-aided Salient Object Detection via Joint Latent Space Embedding.

Abstract

Traditional unsupervised salient object detection methods majorly rely on pre-defined assumptions about saliency. However, these assumptions may not be sufficient for handling test images of varied content and context. Meanwhile, supervised models learn saliency knowledge from thousands of annotated images, which are usually expensive to obtain. In this paper, we propose an exemplar-aided salient object detection method, which can complement heuristic saliency assumptions by leveraging only a few exemplar images. This is a challenging task since the appearances between the query images and the exemplars can be quite different. We handle it by learning the matching relationship of the intra-class instances in a latent embedding space in an online fashion. Given a test image and an annotated reference image (retrieved from several exemplar images), our method transfers the foreground and background information of the reference image to the test image via a joint latent embedding of image superpixels. Extensive experiments show that our method can easily improve the performance of existing unsupervised methods even when a very small reference image dataset (e.g. one image) is used. In addition, our method is able to attain competitive performance against fully supervised methods.