A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiotemporal Saliency in Dynamic Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting salient objects from images and videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
what is the chance of happening: a new way to predict where people look
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Stochastic bottom-up fixation prediction and saccade generation
Image and Vision Computing
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Salient region detection has gained a great deal of attention in computer vision. It is useful for applications such as adaptive video/image compression, image segmentation, anomaly detection, image retrieval, etc. In this paper, we study saliency detection using a center-surround approach. The proposed method is based on estimating saliency of local feature contrast in a Bayesian framework. The distributions needed are estimated particularly using sparse sampling and kernel density estimation. Furthermore, the nature of method implicitly considers what refereed to as center bias in literature. Proposed method was evaluated on a publicly available data set which contains human eye fixation as groundtruth. The results indicate more than 5% improvement over state-of-theart methods. Moreover, the method is fast enough to run in real-time.