Year: 2009
Type: article
Abstract: Reasoning about objects in a visual scene can be substantially improved if we have a representation that includes both information about the object properties and information about the relations that hold between them.For this, we have built a semantic-relational network that makes use of textual commonsense knowledge of both sorts and allows for inference within a Bayesian framework (Röhrbein, Eggert, & Körner, 2007).In order to enrich this graphical representation, we target at adding complementary information gained, for example, from sets of labeled images.We are especially interested in relational knowledge, a demand that rules out most existing image databases since they usually contain only one labeled object per image.LabelMe proved as a promising alternative and we started our efforts by analyzing the statistical dependencies between all objects in fully-labeled images of that database.In the contribution here, we report these results and discuss how the gained information can be used to build contexts as a first important step in reaching the final goal of learning a relational knowledge representation in an autonomous way.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2627043080