Semantic-Improved Color Imaging Applications: It is all about Context

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Semantic-Improved Color Imaging Applications: It Is All About Context

Semantic-Improved Color Imaging Applications: It is all about Context

Abstract

Semantic-Improved Color Imaging Applications: It is all about Context, Multimedia data with associated semantics is omnipresent in today’s social online platforms in the form of keywords, user comments, and so forth. This article presents a statistical framework designed to infer knowledge in the imaging domain from the semantic domain. Note that this is the reverse direction of common computer vision applications. The framework relates keywords to image characteristics using a statistical significance test. The semantic gap is a major challenge to solve in the multimedia community. In this article we investigate the gap in the reverse direction, Bridging the gap in reverse direction might seem counterintuitive at first sight because the classic forward direction is an ubiquitous problem in our daily digital life: we want computers to gain a semantic understanding of digital content in order to ease search, classification and so forth.

Conclusions

Semantic-Improved Color Imaging Applications: It is all about Context,In this article we propose methods and applications that use the semantic domain to infer knowledge and actions in the image domain. This is the opposite direction of classic computer vision applications.

The core of the presented applications is a highly scalable statistical framework to compute associations between image characteristics

We discuss multiple methods to realize the computation of the associations

conclude that the Mann-Whitney-Wilcoxon test is best in terms of both accuracy and computational complexity.

The statistical framework handles millions of images and hundreds of thousands of keywords.