Semantic and Affective Multimedia Retrieval using EEG-based Biological Information

在这项研究中，我们开发了用于多媒体内容(如图像、电影和音乐)的情感信息检索方法，方法是从生物信息(如脑电)中估计用户的偏好/品味。然而，当受试者想象到未知的内容时，很难测量脑电。我们提出了一种基于典型相关的线性回归方法来估计内容特征中的互相关情感特征。作为应用，我们还提出了一种敏感过滤方法，从类似的图像检索方法得到的结果中剔除不愉快的图像，并证实了该方法作为情感检索系统的有效性。

In this study, we exploited methods for affective information retrieval for multimedia contents such as images, movies, and music by estimating user preferences/tastes from biological information such as EEG. It is, however, difficult to measure the EEG when a subject visualizes an unknown contents. We proposed a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation affective features from contents features. As an application, we also proposed a sensitivity filtering method for eliminating unpleasant images from the results obtained with similar image retrieval methods, and confirmed that the proposed method was efficient as an affective retrieval system.