Adaptive Location for Multiple Salient Objects Detection

Abstract

Salient objects detection aims to locate objects that capture human attention within images. Recent progresses in saliency detection have exploited the center prior, to combine with other cues such as background information, object size or region contrast, achieving competitive results. However, previous approaches of center prior supposing salient object locates nearly at image center is very simple, fragile, especially not suitable for multiple salient objects detection, but the assumption is mostly heuristic. In this paper, we present an adaptive location method based on geodesic filtering framework to address these issues. First, we detect salient points by the adjustive color Harris algorithm. Second, we involve the Affinity Propagation (AP) method to automatically cluster the salient points for a coarse objects location. Then, we utilize geodesic filtering framework for a final saliency map by multiplying objects location and size. Experimental results on two more challenging databases of off-center and multiple salient objects demonstrate our approach is more robust to the location variations of salient objects, against state-of-the-art methods for saliency detection.