3 research outputs found
Knowledge-rich Image Gist Understanding Beyond Literal Meaning
We investigate the problem of understanding the message (gist) conveyed by
images and their captions as found, for instance, on websites or news articles.
To this end, we propose a methodology to capture the meaning of image-caption
pairs on the basis of large amounts of machine-readable knowledge that has
previously been shown to be highly effective for text understanding. Our method
identifies the connotation of objects beyond their denotation: where most
approaches to image understanding focus on the denotation of objects, i.e.,
their literal meaning, our work addresses the identification of connotations,
i.e., iconic meanings of objects, to understand the message of images. We view
image understanding as the task of representing an image-caption pair on the
basis of a wide-coverage vocabulary of concepts such as the one provided by
Wikipedia, and cast gist detection as a concept-ranking problem with
image-caption pairs as queries. To enable a thorough investigation of the
problem of gist understanding, we produce a gold standard of over 300
image-caption pairs and over 8,000 gist annotations covering a wide variety of
topics at different levels of abstraction. We use this dataset to
experimentally benchmark the contribution of signals from heterogeneous
sources, namely image and text. The best result with a Mean Average Precision
(MAP) of 0.69 indicate that by combining both dimensions we are able to better
understand the meaning of our image-caption pairs than when using language or
vision information alone. We test the robustness of our gist detection approach
when receiving automatically generated input, i.e., using automatically
generated image tags or generated captions, and prove the feasibility of an
end-to-end automated process
Weakly supervised construction of a repository of iconic images
We present a first attempt at semi-automatically harvesting a dataset of iconic images, namely
images that depict objects or scenes, which arouse associations to abstract topics. Our method
starts with representative topic-evoking images from Wikipedia, which are labeled with relevant
concepts and entities found in their associated captions. These are used to query an online image
repository (i.e., Flickr), in order to further acquire additional examples of topic-specific iconic
relations. To this end, we leverage a combination of visual similarity measures, image clustering
and matching algorithms to acquire clusters of iconic images that are topically connected to the
original seed images, while also allowing for various degrees of diversity. Our first results are
promising in that they indicate the feasibility of the task and that we are able to build a first
version of our resource with minimal supervision
Data from the paper: Weakly supervised construction of a repository of iconic images
We present a first attempt at semi-automatically harvesting a dataset of iconic images. Iconic images are depicting objects or scenes, which arouse associations to abstract topics. Our method starts with representative topic-evoking images from Wikipedia, which are labeled with relevant concepts and entities found in their associated captions. These are used to query an online image repository (i.e., Flickr), in order to further acquire additional examples of topic-specific iconic relations. To this end, we leverage a combination of visual similarity measures, image clustering and matching algorithms to acquire clusters of iconic images that are topically connected to the original seed images, while also allowing for various degrees of diversity. Our first results are promising in that they indicate the feasibility of the task and that we are able to build a first version of our resource with minimal supervision