Adaptive Multi-Level Region Merging for Salient Object Detection

Salient object detection is a long-standing problem in computer vision and plays a critical role in understanding the mechanism of human visual attention. In applications that require object-level prior (e.g. image retargeting), it is desirable that saliency detection highlights holistic objects. Lately over-segmentation techniques such as SLIC superpixel [6], Meanshift [1], and graph-based [3] segmentations are popular among saliency detection due to their usefulness on eliminating background noise and reducing computation cost. However, individual small segments provide little information about global contents. Such schemes have limited capability on modeling global perceptual phenomena. Fig.1 shows a typical example. The entire flower tends to be perceived as a single entity by human visual system. It is easily imagined that saliency computation with the help of coarse segmentation is conducive to highlighting entire object while suppressing background. As it is important to control segmentation level to reflect proper image content, more recent approach benefits from multi-scale strategies to compute saliency on both coarse and fine scales with fusion [4]. [4] merges a region to its neighbor region if it is smaller than pre-defined sizes. The underlying problem may be that scale parameters in [4] are crucial to performance. A salient region may not appear in the proper level if it is smaller than the defined size. On the other hand, large background regions with close colors may not be merged together if they are larger than the defined size. In this paper we propose an alternative solution, namely by quantifying contour strength to generate varied levels. Compared to [4], we use edge/contour strength and a globalization technique during merging. Our contributions include: 1. Develop an adaptive merging strategy for salient object detection rather than using several fixed “scales”. Our method generates intrinsic optimal “scales” when the merging continues. 2. Incorporate additional global information by graph-based spectral decomposition to enhance salient contours. It is useful in salient object rendering. 3. Performance obtained is similar to other state-of-the-art methods even though simple region saliency measurements are adopted for each region. As shown in Fig.2, our framework first performs over-segmentation on an input image by using SLIC superpixels [6], from which merging begins. To acquire holistic contour of salient objects as the merging process proceeds, we propose a modified graph-based merging scheme inspired by [3] which sets out to merge regions by quantifying a pre-defined region comparison criterion. Specifically before merging starts, a globalization