科学研究
Scientific research
您所在的位置:首页>科学研究

A Co-saliency Model of Image Pairs

Abstract

In this paper, we introduce a method to detect co-saliency from an image pair that may have some objects in common. The co-saliency is modeled as a linear combination of the single-image saliency map (SISM) and the multi-image  saliency map (MISM). The first term is designed to describe the local attention, which is computed by using three saliency detection techniques available in literature. To compute the MISM, a co-multilayer graph is constructed by dividing the image pair into a spatial pyramid representation. Each node in the graph is described by two types of visual descriptors, which are extracted from a representation of some aspects of local appearance, e.g., color and texture properties. In order to evaluate the similarity between two nodes, we employ a normalized single-pair SimRank algorithm to compute the similarity score. Experimental evaluation on a number of image pairs demonstrates the good performance of the proposed method on the co-saliency detection task.


Paper 

Hongliang Li, King Ngi Ngan, "A Co-saliency Model of Image Pairs," IEEE Transactions on Image Processing, vol. 20, no. 12, pp. 3365-3375,  2011.  

CoSaliency_1col.pdf

  

Results 

cofig1.jpg

Experimental results for single objects. (a)-(b) and (e)-(f): Original image pairs. (c)-(d) and (g)-(h): Results by our method.

cofig2.jpg

 Experimental results for multiple objects. (a)-(b): Original image pairs. (c)-(d): Results by our method. 


Downloads 

1. Ground truth Used for Comparison
 
   105 test image pairs and ground truth masks can be downloaded from 

cosdata.rar 

2. Our co-saliency results for 105 test image pairs can be downloaded from 

ourcosaliency.rar

3. Source Code (MATLAB)
 
   Source code can be downloaded from 

Cosaliency_v1.0.rar


Note: Test images and ground truth masks have been updated. Each dataset includes 210 images that are consistent with our published paper. 


Acknowledgments
 
 This work was partially supported by NSFC (No.60972109), the Program for New Century Excellent Talents in University (NCET-08-0090) and Sichuan Province Science Foundation for Youths (No. 2010JQ0003).


联系方式:

邮编:611731

实验室地址:成都市高新区(西区)西源大道2006号

电子科技大学信息与通信工程学院

技术支持:成都今网科技

版权所有 © 智能视觉信息处理与通信实验室, 2018