Mapping low-level image features to semantic concepts

Humans tend to use high-level semantic concepts when querying and browsing multimedia databases; there is thus, a need for systems that extract these concepts and make available annotations for the multimedia data. The system presented in this paper satisfies this need by automatically generating semantic concepts for images from their low-level visual features. The proposed system is built in two stages. First, an adaptation of k-means clustering using a non-Euclidean similarity metric is applied to discover the natural patterns of the data in the low-level feature space; the cluster prototype is designed to summarize the cluster in a manner that is suited for quick human comprehension of its components. Second, statistics measuring the variation within each cluster are used to derive a set of mappings between the most significant low-level features and the most frequent keywords of the corresponding cluster. The set of the derived rules could be used further to capture the semantic ...

• Type: Conference
• Year: 2001
• Where: SPIESR
• Authors: Daniela Stan, Ishwar K. Sethi
• Keywords: Database, Low-level Features, Low-level Visual Features, Semantic Concepts, SPIESR 2001