Fuzzy Approach for Semantic Face Image Retrieval

Abstract

Our aim is to realize semantic face image retrieval (FIR)\ by bridging the semantic-gap between low-level visual features of images and the high-level labels that describe facial features. This allows a user to retrieve face images based on a description of face features rather than an example image. We approach the semantic-gap problem by developing a fuzzy-based method of finding a mapping between the low-level features and the vocabulary that describes the semantics of the face features. The contribution of this paper is a new method of using fuzzy clustering and fuzzy inference methods to derive the degree of membership for each semantic label to a new image. Our experiments show that our approach has good results for annotating images, and provides a sound foundation for local face feature-based FIR systems. We have made available a demonstration system online. Further, our system is not domain specific and can be generalized and applied to other problems in the field of image retrieval.