Indexing natural images for retrieval based on Kansei factors

Current image indexing methods are based on measures of visual content. However, this approach provides only a partial solution to the image retrieval problem. For example, an artist might want to retrieve an image (for use in an advertising campaign) that evokes a particular “feeling” in the viewer. One technique for measuring evoked feelings, which originated in Japan, indexes images based on the inner impression (i.e. the kansei) experienced by a person while viewing an image or object – impressions such as busy, elegant, romantic, or lavish. The aspects of the image that evoke this inner impression in the viewer are called kansei factors. The challenge in kansei research is to enumerate those factors, with the ultimate goal of indexing images with the “inner impression” that viewers experience. Thus, the focus is on the viewer, rather than on the image, and similarity measures derived from kansei indexing represent similarities in inner experience, rather than visual similarity. This paper presents the results of research that indexes images based on a set of kansei impressions, and then looks for correlations between that indexing and traditional content-based indexing. The goal is to allow the indexing of images based on the inner impressions they evoke, using visual content.