Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contrast-based image attention analysis by using fuzzy growing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
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
IEEE Transactions on Image Processing
Bilinear deep learning for image classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Capturing a great photo via learning from community-contributed photo collections
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Photos taken by human beings significantly differ from the pictures that are taken by a surveillance camera or a vision sensor on a robot, e.g., human beings may intentionally capture photos to express his/her feeling or record a memorial scene. Such a creative photo capture process is accomplished by adjusting two factors: (1) the parameters setting of a camera; and (2) the position between the camera and the interesting objects or scenes. To enable automatic understanding and interpretation of the semantics of photos, it is very important to take all these factors into account. Unfortunately, most existing algorithms for image understanding focus on only the content of the images while completely ignoring these two important factors. In this paper, we have developed a new algorithm to calculate what the interestingness of the photographer is and what the core content of a photo is. The gained information (i.e., attended regions and attention of the photographer) is further used to support more effective photo classification and retrieval. Our experiments on 70,000+ photos taken by 200+ different models of cameras have obtained very positive results.