A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
ACM SIGGRAPH 2006 Papers
Super-resolution without explicit subpixel motion estimation
IEEE Transactions on Image Processing
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency estimation using a non-parametric low-level vision model
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Automatic foveation for video compression using a neurobiological model of visual attention
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
Letters: Background contrast based salient region detection
Neurocomputing
Saliency detection based on integrated features
Neurocomputing
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Visual saliency is an important and indispensable part of visual attention. We present a novel saliency detection model using Bayes' theorem. The proposed model measures the pixel saliency by combining local kernel density estimation of features in center-surround region and global density estimation of features in the entire image. Based on the model, a saliency detection method is presented that extracts the intensity, color and local steering kernel features and employs feature level fusion method to obtain the integrated feature as the corresponding pixel feature. Experimental results show that our model outperforms the current state-of-the-art models on human visual fixation data.