Real-time salient object detection with a minimum spanning tree

• Main Authors: TU, Wei-Chih, HE, Shengfeng, YANG, Qingxiong, CHIEN, Shao-Yi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Background region; Dissimilarity measures; Distance transform algorithms; Distance transforms; Minimum spanning trees; Object geometries; Salient object detection; State-of-the-art methods; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/8431; https://ink.library.smu.edu.sg/context/sis_research/article/9434/viewcontent/Real_Time_Salient_Object_Detection_With_a_Minimum_Spanning_Tree.pdf
• Institution: Singapore Management University

In this paper, we present a real-time salient object detection system based on the minimum spanning tree. Due to the fact that background regions are typically connected to the image boundaries, salient objects can be extracted by computing the distances to the boundaries. However, measuring the image boundary connectivity efficiently is a challenging problem. Existing methods either rely on superpixel representation to reduce the processing units or approximate the distance transform. Instead, we propose an exact and iteration free solution on a minimum spanning tree. The minimum spanning tree representation of an image inherently reveals the object geometry information in a scene. Meanwhile, it largely reduces the search space of shortest paths, resulting an efficient and high quality distance transform algorithm. We further introduce a boundary dissimilarity measure to compliment the shortage of distance transform for salient object detection. Extensive evaluations show that the proposed algorithm achieves the leading performance compared to the state-of-the-art methods in terms of efficiency and accuracy.