Efficient Salient Region Detection with Soft Image Abstraction

• Published in: 2013 IEEE International Conference on Computer Vision pp. 1529 - 1536
• Main Authors: Ming-Ming Cheng, Warrell, Jonathan, Wen-Yan Lin, Shuai Zheng, Vineet, Vibhav, Crook, Nigel
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.12.2013
• Subjects: Abstracts; Computational efficiency; Computer vision; Conferences; Correlation; Estimation; Graphical models; Histograms; image abstraction; Image color analysis; Image detection; object of interest segmentation; Permissible error; Pixels; Representations; salient object detection; Similarity; Spatial distribution; visual attention; Visualization
• ISSN: 1550-5499
• DOI: 10.1109/ICCV.2013.193

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.