## 7 Conclusions  
This study aimed to investigate the relationship between the two most common emotion models used in affective picture databases: the dimensional and discrete models. We did not attempt to create a new larger emotion model as a union of the two existing models but rather to explore if it is possible to identify statistically relevant dependencies between individual discrete emotions and discrete emotions and emotion dimensions. The experiment determined a set of statistical dependencies between discrete and dimensional emotion variables using the NAPS and NAPS BE databases, containing 510 and 1356 emotionally annotated pictures, respectively. In the presented research, distance metrics in discrete and dimensional emotion space have been described. The correlated pairs of discrete emotions in picture stimuli have been reported with the highest positive correlation between tuples of discrete emotions [anger, sadness], {fear, surprise}, and {disgust, surprise}, and negative correlation values with {happiness, sadness} and {happiness, anger}. Significant positive correlation was observed between discrete and dimensional emotions tuples {happiness, valence}, {arousal, fear}, and {arousal, surprise}. Media with these predominant discrete and dimensional emotions are mutually complementary in eliciting specific affective states. Using these findings, researchers who employ affective multimedia databases, such as psychologists, psychiatrists, and neurologists, may select the best available appropriate multimedia stimuli if the optimal picture stimulations are not accessible. Nevertheless, the selection of stimuli based only on emotional content is insufficient in provoking desired emotional reactions. Therefore, the semantic meaning of multimedia must be selected with the greatest care so that the stimuli are effective and have the desired personal importance and ego relevance to the participants of an emotion elicitation experiment [63]. The NAPS and NAPS BE selected for the study are the largest general affective picture databases with dimensional and discrete annotations available. However, a validation experiment using more recent databases in the future should also be considered to confirm the reported findings, such as the OASIS [25]. There are many possible practical uses of the presented research. Ultimately, we would like to implement the attained findings in an intelligent system that can reason about the emotional content of a multimedia document. Transforming information about emotions between different models would help to discover hidden knowledge in multimedia documents. This would facilitate the generation of additional affective picture annotations and the construction of a more detailed and comprehensive description of database content. Such an intelligent system could potentially have many useful applications, such as supported construction of affective multimedia databases, video recommendation, or emotion estimation. Finally, the presented research has not investigated complex relationships between emotional norms and multimedia semantics. The supported construction of affective multimedia databases is much needed and will lead to better utilization in practice. Because current construction processes require the evaluation of many subjective self-reports collected in stimuli annotation experiments, the workload and resources needed to perform these tasks hinder the employment of affective multimedia databases. Therefore, automation of the stimuli annotation process is necessary to facilitate the development of affective multimedia databases and propagate their usage. The congruency of semantics and emotion allows us to obtain a priori knowledge about stimulus semantics to derive its emotion ratings using semantic and emotion annotations stored in the affective multimedia databases [63]. In such a construction process, the congruency may be used to retrieve a set of the most likely emotion annotations for a new multimedia stimulus.