Representing pictures with emotions
This work addresses the challenge of enriching image retrieval with human semantics for users in multimedia and AI domains, but it is incremental as it builds on existing methods like object ontology.
The paper tackles the problem of bridging the semantic gap in content-based image retrieval by using a codified emotion ontology over global color features to annotate images at a high semantic level, showing that controlled random sampling can capture high-level emotional concepts with significant relevance as monitored by entropy measures.
Modern research in content-based image retrieval systems (CIBR) has become progressively more focused on the richness of human semantics. Several approaches may be used to reduced the 'semantic gap' between the high-level human experience and the low level visual features of pictures. Object ontology, among others, is one of the methods. In this paper we investigate the use of a codified emotion ontology over global color features of images to annotate the images at a high semantic level. In order to speed up the annotation process the images are sampled so that each digital image is represented by a random subset of its content. We test within controlled conditions how this random subset may represent the adequate high level emotional concept presented in the image. We monitor this information reducing process with entropy measures, showing that controlled random sampling can capture with significant relevance high level concepts for picture representation.