A location-aware scale-space method for salient object detection

Many existing saliency detection methods made an assumption that the salient object is on the center of the image and incorporated such center-biased assumption in the design of their algorithms. Obviously, this is not always proper to set, especially for those imageries acquired by unmanned monitoring system or device (e.g., surveillance camera), in which the salient object could appear in any location within the image. Consequently, the resulted saliency detection performance could be greatly degraded. In this paper, an existing hypercomplex Fourier transform (HFT) based saliency detection algorithm is investigated and modified for improving the saliency detection performance. In details, we remove its prior assumption on `center bias´ and exploit a location-aware strategy to identify the optimal saliency map across multiple scales of the image. Extensive simulation results have justified that the proposed location-aware HFT-based approach clearly outperforms existing five state-of-the-art algorithms on saliency detection.