Visual-semantic graphs: using queries to reduce the semantic gap in web image retrieval

We explore the application of a graph representation to model similarity relationships that exist among images found on the Web. The resulting similarity-induced graph allows us to model in a unified way different types of content-based similarities, as well as semantic relationships. Content-based similarities include different image descriptors, and semantic similarities can include relevance user feedback from search engines. The goal of our representation is to provide an experimental framework for combining apparently unrelated metrics into a unique graph structure, which allows us to enhance the results of Web image retrieval. We evaluate our approach by re-ranking Web image search results. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval; H.2.8 [Database Applications]: Image Databases General Terms Algorithms, Experimentation, Performance Keywords Web Image Search, Web Image Re-ranking, Query Log Analysis, Content-b...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Barbara Poblete, Benjamin Bustos, Marcelo Mendoza, Juan Manuel Barrios
• Keywords: CIKM 2010, Content-based Similarities, Image, Information Technology, Web Image