Evaluation of Keyword Search in Affective Multimedia Databases
| Autor: | Marko Horvat, Marin Vuković, źeljka Car |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Information retrieval
Database Multimedia business.industry Computer science Lift (data mining) Keyword search Lexical similarity 020207 software engineering Usability 02 engineering and technology Formality computer.software_genre Semantic data model Data modeling 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Affective computing Affective multimedia Classification Semantic annotation Emotion computer Natural language processing |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783662495209 |
| DOI: | 10.1007/978-3-662-49521-6_3 |
| Popis: | Multimedia documents such as pictures, videos, sounds and text provoke emotional responses of different intensity and polarity. These stimuli are stored in affective multimedia databases together with description of their semantics based on keywords from unsupervised glossaries, expected emotion elicitation potential and other important contextual information. Affective multimedia databases are important in many different areas of research, such as affective computing, human-computer interaction and cognitive sciences, where it is necessary to deliberately modulate emotional states of individuals. However, restrictions in the employed semantic data models impair retrieval performance measures thus severely limiting the databases’ overall usability. An experimental evaluation of multi-keyword search in affective multimedia databases, using lift charts as binomial classifiers optimized for retrieval precision or sensitivity, is presented. Suggestions for improving expressiveness and formality of data models are elaborated, as well as introduction of dedicated ontologies which could lead to better data interoperability. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|