# Representing pictures with emotions

## TL;DR

This paper explores using a codified emotion ontology combined with random sampling of image content to improve high-level semantic annotation in content-based image retrieval systems, aiming to bridge the semantic gap.

## Contribution

It introduces a novel approach of applying emotion ontologies and controlled random sampling to enhance semantic annotation of images.

## Key findings

- Controlled random sampling effectively captures high-level emotional concepts.
- Entropy measures indicate the sampling process preserves semantic information.
- The method speeds up annotation without significant loss of semantic accuracy.

## Abstract

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.