Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Cluster Isolation Criterion Based on Dissimilarity Increments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian network-based framework for semantic image understanding
Pattern Recognition
Bayesian fusion of camera metadata cues in semantic scene classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Photographs, which are taken by human beings with creative thinking, may significantly differ from the images that are taken by a surveillance camera or a visual sensor on a robot. Human being intentionally shoot a photograph to express his/her feeling or photo-realistically record a scene by adjusting two factors: the parameters setting of a camera and the position between the camera and the object which he or she is interested in. Based on these observations, a graph model based stochastic method is used to discover the pattern of how people taking photos, so that the interesting regions of the images can be determined automatically. Both the visual features of the images and the camera metadata parameters are simultaneously taken into considered. Experimental evaluation on over 7000+ photos taken by 200+ different models of cameras with variety of interests has shown the robustness of our techniques.