Efficient salient region detection with soft image abstraction

• Main Authors: CHENG, Ming-Ming, WARRELL, Jonathan, LIN, Wen-yan, ZHENG, Shuai, VINEET, Vibhav, CROOK, Nigel
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: image abstraction; object of interest segmentation; salient object detection; visual attention; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/4804; https://ink.library.smu.edu.sg/context/sis_research/article/5807/viewcontent/Cheng_ICCV2013.pdf
• Institution: Singapore Management University

Detecting visually salient regions in images is one of the fundamental problems in computer vision. We propose a novel method to decompose an image into large scale perceptually homogeneous elements for efficient salient region detection, using a soft image abstraction representation. By considering both appearance similarity and spatial distribution of image pixels, the proposed representation abstracts out unnecessary image details, allowing the assignment of comparable saliency values across similar regions, and producing perceptually accurate salient region detection. We evaluate our salient region detection approach on the largest publicly available dataset with pixel accurate annotations. The experimental results show that the proposed method outperforms 18 alternate methods, reducing the mean absolute error by 25.2% compared to the previous best result, while being computationally more efficient.