Semantic inter-media image retrieval in photographic collections

In this paper we propose a method for semantic inter-media photographic retrieval, exploiting the advantages of both textual and content-based frameworks: the relatively high initial precision and diversity of results from visual and text retrieval, and the robust overall precision and recall of text retrieval. The method employs simple block-based visual retrieval which, at early precision, outperforms MPEG-7 features ScalableColor, ColorLayout and EdgeHistogram, allowing for reliable auto-relevance feedback. The query expansion applied respects the semantic constraints of the original query and enhances precision and confidence through redundancy. The proposed method was tested on a benchmark data set of 20,000 diverse tourist photographs and obtained promising results.