A note on the gradient of a multi-image
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
CORAL: using natural language generation for navigational assistance
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Boosting Color Saliency in Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual attention detection in video sequences using spatiotemporal cues
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Improving the Graph-Based Image Segmentation Method
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Sketch2Photo: internet image montage
ACM SIGGRAPH Asia 2009 papers
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Resizing by symmetry-summarization
ACM SIGGRAPH Asia 2010 papers
Predictive Saliency Maps for Surveillance Videos
DCABES '10 Proceedings of the 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science
Learning to Detect a Salient Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual saliency detection by spatially weighted dissimilarity
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Saliency estimation using a non-parametric low-level vision model
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Global contrast based salient region detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
The JPEG2000 still image coding system: an overview
IEEE Transactions on Consumer Electronics
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For many applications in graphics, design and human computer interaction, it is essential to reliably estimate the visual saliency of images. In this paper, we propose a visual saliency detection method that combines the respective merits of color saliency boosting and global region based contrast schemes to achieve more accurate saliency maps. Our method is compared with existing saliency detection methods when evaluated using four public available datasets. Experimental results show that our method consistently outperformed current state-of-the-art methods on predicting human fixations. We also demonstrate how the extracted saliency map can be used for image classification.