People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant) for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images. 1. Introduction Thanks to recent improvements in the processing speed of computers and the sophistication of information retrieval algorithms, it is possible to retrieve relevant information from huge databases in an efficient manner. However, many retrieval systems cannot perform their tasks at a level that satisfies the user’s needs. Thus, there is a need for an information retrieval system that satisfies the tastes of users. In general, user tastes (preferences) can be predicted from the results of polls completed by users or based on the utilization status of the system. A considerable amount of researches has been conducted to predict preference information from web browsing histories, past Internet purchases, and so forth [1–6]. However, the web browsing history does not necessarily reflect the preferences of users, because the amount of interest also depends on the objectives of browsing or purchasing. Thus,
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