Adaptive histogram equalization and its variations
Computer Vision, Graphics, and Image Processing
WordNet: a lexical database for English
Communications of the ACM
IEEE Computer Graphics and Applications
ACM SIGGRAPH 2006 Papers
Content Aware Image Enhancement
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The MIR flickr retrieval evaluation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Harmonic colorization using proportion contrast
Proceedings of the 7th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa
Data-driven image color theme enhancement
ACM SIGGRAPH Asia 2010 papers
Automatic image semantic interpretation using social action and tagging data
Multimedia Tools and Applications
Example-based image color and tone style enhancement
ACM SIGGRAPH 2011 papers
Defocus map estimation from a single image
Pattern Recognition
Towards automatic concept transfer
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering
Visual and semantic similarity in ImageNet
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image enhancement via adaptive unsharp masking
IEEE Transactions on Image Processing
Semantic awareness for automatic image interpretation
Proceedings of the 20th ACM international conference on Multimedia
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With the advent of social image-sharing communities, millions of images with associated semantic tags are now available online for free and allow us to exploit this abundant data in new ways. We present a fast non-parametric statistical framework designed to analyze a large data corpus of images and semantic tag pairs and find correspondences between image characteristics and semantic concepts. We learn the relevance of different image characteristics for thousands of keywords from one million annotated images. We demonstrate the framework's effectiveness with three different examples of semantic image enhancement: we adapt the gray-level tone-mapping, emphasize semantically relevant colors, and perform a defocus magnification for an image based on its semantic context. The performance of our algorithms is validated with psychophysical experiments.