Low-Level Salient Region Detection Using Multi-Scale Filtering

Various computer visions tasks require a summary of visually important regions in an image. Thus, developing simple yet accurate salient region detection algorithms has become an important research topic. The currently best performing state-of-the-art saliency detection algorithms incorporate image segmentation for abstraction. However, errors introduced in this step of the algorithms are transferred to the final saliency map estimation. In order to avoid this problem, we propose a simple low-level salient region detection algorithm that uses multi-scale filters. We consider each possible combination of filtered image pairs as weak saliency maps and combine them according to their adaptively computed compactness and center prior. Our filterbased method successfully eliminates the texture in the background and gives relatively uniform salient regions for multi-colored objects. In addition, the combination of several multi-scale filters produces a full-resolution saliency output, which preserves object boundaries. We show that our algorithm outperforms the most recent state-of-the-art methods on a database of 1000 images with pixel-precision ground truths