Semantic Image Enhancement ‒ IVRL ‐ EPFL

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)

2012

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

p. 489-498

DOI : 10.1145/2393347.2393417

Detailed record

Full text – View at publisher

Example Images

Semantic tone-mapping:

input
output: dark	output: sand

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

Color enhancement:

input

output: strawberry

See more example images for color re-rendering.

Depth-of-field adaptation:

input

output: macro

See more example images for spatial frequency re-rendering.

Supplementary material for the statistical framework

In this publication we use the Mann-Whitney-Wilcoxon (MWW) test to determine the significance values z. There are of course other tests that also estimate whether two distributions are significantly different from each other. An overview is given here, together with an explanation why we favor the MWW test.