Paper Info
Title
Panoramic Image Saliency Detection by Fusing Visual Frequency Feature and Viewing Behavior Pattern.
Abstract
The panoramic images are widely used in many applications. Saliency detection is an important task for panoramic image processing. Traditional saliency detection algorithms that are originally designed for conventional flat-2D images are not efficient for panoramic images due to their particular viewing way. Based on this consideration, we propose a novel saliency detection algorithm for panoramic images by fusing visual frequency feature and viewing behavior pattern. By extracting the spatial frequency information in viewport domain and computing the centersurround contrast of them for the whole panoramic image, the visual frequency feature for saliency detection is accurately obtained. Further more, the context of user's viewing behavior is integrated with visual frequency feature to generate the final saliency map. The experimental results show that the proposed algorithm is superior to the state-of-theart algorithms when Pearson Correlation Coefficient (CC) is used as the evaluation metric.
Year
DOI
Venue
2018
10.1007/978-3-030-00767-6_39
ADVANCES IN MULTIMEDIA INFORMATION PROCESSING - PCM 2018, PT II
Keywords
Field
DocType
Panoramic image,Saliency detection,Viewport,Virtual reality
Computer vision,Behavioral pattern,Pearson product-moment correlation coefficient,Saliency map,Virtual reality,Pattern recognition,Viewport,Salience (neuroscience),Computer science,Image processing,Artificial intelligence,Spatial frequency
Conference
Volume
ISSN
Citations 
11165
0302-9743
1
PageRank 
References 
Authors
0.36
8
6
Name
Order
Citations
PageRank
Ying Ding1887.62
Yanwei Liu27014.92
Jinxia Liu36011.61
Kedong Liu410.36
Liming Wang521.41
Zhen Xu62117.33