Security-oriented picture-in-picture visual modifications
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Quaternion-Based spectral saliency detection for eye fixation prediction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Saliency modeling from image histograms
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Visual saliency detection with center shift
Neurocomputing
Efficient visual attention based framework for extracting key frames from videos
Image Communication
Visual saliency detection using information divergence
Pattern Recognition
Optimal contrast based saliency detection
Pattern Recognition Letters
Spatiotemporal saliency detection and salient region determination for H.264 videos
Journal of Visual Communication and Image Representation
Selection of a best metric and evaluation of bottom-up visual saliency models
Image and Vision Computing
Robotics and Autonomous Systems
Saliency detection based on integrated features
Neurocomputing
A New Framework for Multiscale Saliency Detection Based on Image Patches
Neural Processing Letters
Ensemble dictionary learning for saliency detection
Image and Vision Computing
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We introduce a simple image descriptor referred to as the image signature. We show, within the theoretical framework of sparse signal mixing, that this quantity spatially approximates the foreground of an image. We experimentally investigate whether this approximate foreground overlaps with visually conspicuous image locations by developing a saliency algorithm based on the image signature. This saliency algorithm predicts human fixation points best among competitors on the Bruce and Tsotsos [1] benchmark data set and does so in much shorter running time. In a related experiment, we demonstrate with a change blindness data set that the distance between images induced by the image signature is closer to human perceptual distance than can be achieved using other saliency algorithms, pixel-wise, or GIST [2] descriptor methods.