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

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.


Published in:
Proceedings of the 20th ACM international conference on Multimedia (MM'12), 489-498
Presented at:
ACM Multimedia, Osaka, Japan, October 29 - November 2, 2012
Year:
2012
ISBN:
978-1-4503-1089-5
Keywords:
Note:
Source code available under a creative commons license
Laboratories:




 Record created 2012-10-07, last modified 2018-03-17

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