Semantic Image Enhancement

Joint Statistical Analysis of Images and Keywords with Applications in Semantic Image Enhancement

A. Lindner; A. Shaji; N. Bonnier; S. Süsstrunk 

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.

Proceedings of the 20th ACM international conference on Multimedia (MM’12)


ACM Multimedia, Osaka, Japan, October 29 – November 2, 2012.

p. 489-498

DOI : 10.1145/2393347.2393417

Example Images

Semantic tone-mapping:

dark/sand before
output: dark
dark after
output: sand
sand after

See more example images for semantic tone-mapping and the psychophysical experiments.

Color enhancement:

dark after
output: strawberry
sand after

See more example images for color re-rendering.

Depth-of-field adaptation:

dark after
output: macro
sand after

See more example images for spatial frequency re-rendering.

Supplementary material for the statistical framework


Download Matlab package with pre-computed significance values (40 MByte).

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.


Questions or feedback are welcome. Please mail the author or visit his website.

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