Our research group has been working on attractiveness prediction, reasoning, and even enhancement for multimedia content, which we call “attractiveness computing.” Attractiveness includes impressiveness, instagrammability, memorability, clickability, and so on. Analyzing such attractiveness was usually done by experienced professionals but we have experimentally revealed that artificial intelligence (AI) based on big multimedia data can imitate or reproduce professionals' skills in some cases. In this paper, we introduce some of the representative works and possible real-life applications of our attractiveness computing for image media.

Most people are concerned about their appearance, and the easiest way to change the appearance is to change the hairstyle. However, except for professional hairstylists, it is difficult to objectively judge which hairstyle suits them. Currently, oval faces are generally said to be the ideal facial shape in terms of suitability to various hairstyles. Meanwhile, field of visual perception (FVP), proposed recently in the field of cognitive science, has attracted attention as a model to represent the visual perception phenomenon. Moreover, a computation model for digital images has been proposed, and it is expected to be used in quantitative evaluation of sensibility and sensitivity called “kansei.” Quantitative evaluation of “goodness of patterns” and “strength of impressions” by evaluating distributions of the field has been reported. However, it is unknown whether the evaluation method can be generalized for use in various subjects, because it has been applied only to some research subjects, such as characters, text, and simple graphics. In this study, for the first time, we apply FVP to facial images with various hairstyles and verify whether it has the potential of evaluating impressions of female faces. Specifically, we verify whether the impressions of facial images that combine various facial shapes and female hairstyles can be represented using FVP. We prepare many combinational images of facial shapes and hairstyles and conduct a psychological experiment to evaluate their impressions. Moreover, we compute the FVP of each image and propose a novel evaluation method by analyzing the distributions. The conventional and proposed evaluation values correlated to the psychological evaluation values after normalization, and demonstrated the effectiveness of the FVP as an image feature quantity to evaluate faces.

We present the concept of folksonomical peer-to-peer (P2P) file sharing networks that allow participants (peers) to freely assign structured search tags to files. These networks are similar to folksonomies in the present Web from the point of view that users assign search tags to information distributed over a network. As a concrete example, we consider an unstructured P2P network using vectorized Kansei (human sensitivity) information as structured search tags for file search. Vectorized Kansei information as search tags indicates what participants feel about their files and is assigned by the participant to each of their files. A search query also has the same form of search tags and indicates what participants want to feel about files that they will eventually obtain. A method that enables file search using vectorized Kansei information is the Kansei query-forwarding method, which probabilistically propagates a search query to peers that are likely to hold more files having search tags that are similar to the query. The similarity between the search query and the search tags is measured in terms of their dot product. The simulation experiments examine if the Kansei query-forwarding method can provide equal search performance for all peers in a network in which only the Kansei information and the tendency with respect to file collection are different among all of the peers. The simulation results show that the Kansei query forwarding method and a random-walk-based query forwarding method, for comparison, work effectively in different situations and are complementary. Furthermore, the Kansei query forwarding method is shown, through simulations, to be superior to or equal to the random-walk based one in terms of search speed.

Commercial breaks are often placed at the climax of stories in recent TV programs in Japan, which may cause some serious effects on audiences, especially children, since this practice disturbs the concentrations. The experiment measured the psycho-physiological state of four children before and after commercials. The results showed that the next peak of attention is delayed by distracting the attention.

This paper surveys recent research activities on three major areas of digital media information base, namely, video database systems as a typical example of temporal application, database systems for mixed reality as an instance of spatial application, and kansei management for digital media retrieval as a case of humanistic feelings application. Current research results by the project Advanced Database Systems for Integration of Media and User Environments are reported.