A Graph-based Bottom-up Salient Object Detection Framework Using Objectness Cues and Multilayer Cellular Automata
Keywords:
Graph-based saliency, Image segmentation, Multilayer cellular automata, Objectness cues, Saliency map fusion, Salient object detection, Superpixel geodesic distanceAbstract
In the digital era, salient object detection (SOD), which focuses on locating and extracting visually prominent objects from an image, plays a crucial role in various computer vision applications, including image segmentation, object recognition, and scene understanding. In this proposed work, an effective saliency detection framework is presented by fusing multiple complementary cues to improve detection accuracy. Initially, saliency maps are generated using a bottom-up saliency detection approach combined with a graph-based salient object detection method and objectness cues, which help in highlighting potential object regions. A graph is constructed by computing the geodesic distances among all superpixels derived from the saliency map, enabling the modeling of global and local relationships between image regions. To further enhance the consistency and accuracy of the detected salient regions, saliency optimization is performed using a multilayer cellular automata mechanism. This optimization process refines the saliency values by propagating information across multiple layers, leading to well-defined object boundaries and improved foreground-background separation. Extensive experimental evaluations conducted on benchmark datasets demonstrate that the proposed method consistently outperforms several existing bottom-up saliency detection approaches in terms of precision, recall, and visual quality.
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