A Co-Saliency Model of Image Pairs

Authors:
Hongliang Li;King Ngi Ngan
Affiliations:
School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China;Department of Electronic Engineering, The Chinese University of Hong Kong,
Venue:
IEEE Transactions on Image Processing
Year:
2011

Citing 0
Cited 8

Video object cosegmentation

Proceedings of the 20th ACM international conference on Multimedia
Salient object detection: a benchmark

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Saliency detection using joint spatial-color constraint and multi-scale segmentation

Journal of Visual Communication and Image Representation
WaveLBP based hierarchical features for image classification

Pattern Recognition Letters
Mode dependent loop filter for intra prediction coding in H.264/AVC

Journal of Visual Communication and Image Representation
Superpixel based color contrast and color distribution driven salient object detection

Image Communication
Tag-Saliency: Combining bottom-up and top-down information for saliency detection

Computer Vision and Image Understanding
SalientShape: group saliency in image collections

The Visual Computer: International Journal of Computer Graphics

Quantified Score

Hi-index	0.01

Visualization

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.