Avaliação do instrumento SAM para a etiquetagem de atributos afetivos de imagens em ambiente web

Abstract

In an image retrieval system, concerning to the images semantics, several levels of query can emerge, from simple statements as “find pictures with a boat” to more abstract ones, such as “find pictures depicting happy atmospheres”. Usually, the abstract level refers to the affective or emotional content of the images and is considered a relevant dimension in which users specify their queries. Clearly, due to its inherent complexity, affect is difficult to model and consequently quite difficult to be handled by a retrieval system. A broadly accepted psychological method to capture the images affective attributes is the one based on the differential scales of valence (pleased- unpleased) and arousal (calm-excited), associated with the Self Assessment Manikin (SAM) instrument. In this work, we present an evaluation of the SAM instrument in the context of the image tagging, operating in a web environment. To perform these studies and capture as many as possible users’ tags, we developed a live web application named Get Your Mood (GYM) (http://getyourmood.com). The GYM employs the metaphor of a mood test in order to stimulate and attract the users to execute the valence/arousal tagging. Approximately 80 tags were collected for each of the 40 selected images from the image database International Affective Picture System (IAPS). The statistical analysis indicated that there is no significant difference between the IAPS valence/arousal mean values and the valence/arousal mean values obtained with the GYM. This fact provides evidences that the SAM instrument remains effective, in spite of the application under distinct conditions if compared to those claimed in its traditional protocol. Thus, it is possible to conclude that the valence/arousal dimensions, associated with the SAM instrument, can be explored as an efficient approach to the affective tagging of images in the web. In addition, we developed a database with 104 images and 50 valence/arousal tags were collect for each one. In opposite to the IAPS, the images of this novel database, named Open Affective Images (OPAFI), can be easily obtained and published without restrictions. Hence, the OPAFI database and the associated affective scores are interesting resources to other researchers, both from the computer vision or psychology communities.