Salient object detection via google image retrieval

Abstract

Among trillions of images available online, there likely exists images whose content is visually similar to the query image (source image). Based on this observation, we propose a novel approach for salient object detection with the help of retrieved images returned by Google image search. We take the regional saliency of the source image as the frequency of occurrences in the retrieved images. The procedure of our saliency estimation approach is as follows. Firstly, given a query (source image) we extract N similar images from the retrieved result returned by Google. Then, we conduct matching between the source image and the extracted retrieval images in order to detect the repetitive region among them. Simultaneously, we segment the source image into several distinctive regions using superpixel segmentation and fusion. Finally, we derive the saliency map from the matching on the segmented regions. Experimental results demonstrate that compared with other methods, the proposed approach consistently achieves higher saliency detection performance in terms of subjective observations and objective evaluations.