Affiliation: | 1 Digital Media Lab, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, P.R. China; 2 Research Center of Digital Media, Graduate School of the Chinese Academy of Sciences, Beijing 100049, P.R. China |
Abstract: | Current investigations on visual information retrieval are generally content-based methods. The significant difference between similarity in low-level features and similarity in high-level semantic meanings is still a major challenge in the area of image retrieval. In this work, a scheme for constructing visual ontology to retrieve art images is proposed. The proposed ontology describes images in various aspects, including type & style, objects and global perceptual effects. Concepts in the ontology could be automatically derived. Various art image classification methods are employed based on low-level image features. Non-objective semantics are introduced, and how to express these semantics is given. The proposed ontology scheme could make users more naturally find visual information and thus narrows the “semantic gap”. Experimental implementation demonstrates its good potential for retrieving art images in a human-centered manner. Supported by China-American Digital Academic Library (CADAL) project, partially supported by the Research Project on Context-Based Multiple Digital Media Semantic Organization and System Development (Grant No. op2004001); and the One-Hundred Talents Plan of CAS (Grant No. m2041). |