| 1 |
Characterization and classification of semantic image-text relations ...
|
|
|
|
Abstract:
The beneficial, complementary nature of visual and textual information to convey information is widely known, for example, in entertainment, news, advertisements, science, or education. While the complex interplay of image and text to form semantic meaning has been thoroughly studied in linguistics and communication sciences for several decades, computer vision and multimedia research remained on the surface of the problem more or less. An exception is previous work that introduced the two metrics Cross-Modal Mutual Information and Semantic Correlation in order to model complex image-text relations. In this paper, we motivate the necessity of an additional metric called Status in order to cover complex image-text relations more completely. This set of metrics enables us to derive a novel categorization of eight semantic image-text classes based on three dimensions. In addition, we demonstrate how to automatically gather and augment a dataset for these classes from the Web. Further, we present a deep learning ...
|
|
Keyword:
020; Data augmentation; Image-text class; Multimodality; Semantic gap
|
|
URL: https://dx.doi.org/10.34657/3700
https://oa.tib.eu/renate/handle/123456789/5071
|
|
BASE
|
|
Hide details
|
|
|
| 2 |
40th EASD Annual Meeting of the European Association for the Study of Diabetes : Munich, Germany, 5-9 September 2004.
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
