The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
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.
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Deictic codes for the embodiment of cognition
Ballard, D. H., Hayhoe, M. M., Pook, P. K., & Rao, R. P. N. (1997) · Behavioral and Brain Sciences · doi:10.1017/s0140525x97001611 · Cited by 1,082
Cited by
Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework
Zhang, D., Meng, D., & Han, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2567393 · Cited by 508
Cluster-Based Co-Saliency Detection
Fu, H., Cao, X., & Tu, Z. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2260166 · Cited by 427
Review of Visual Saliency Detection With Comprehensive Information
Cong, R., Lei, J., Fu, H., Cheng, M.-M., Lin, W., & Huang, Q. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2870832 · Cited by 419
Detection of Co-salient Objects by Looking Deep and Wide
Zhang, D., Han, J., Li, C., Wang, J., & Li, X. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0907-4 · Cited by 328
QualityNet: Segmentation quality evaluation with deep convolutional networks
Huang, C., Wu, Q., & Meng, F. (2016) · 2016 Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip.2016.7805585 · Cited by 40
Metadata
- Authors
- Li, H., & Ngan, K. N.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2011-12
- Volume
- 20
- Issue
- 12
- Pages
- 3365-3375
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2112553126
- Cited by
- 319
- References
- 2
- FWCI
- 24.15
- Type
- journal article
- Language
- en
- Open access
- closed
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