Enhancing Query Expansion for Tag-based Social Image Retrieval

Abstract

Recently, extensive research efforts have been dedicated to tag-based social image search which enables users to formulate their queries using tags. However, tag queries are often ambiguous and typically short yielding to retrieve irrelevant images in top ranked list. To overcome this problem, an effective strategy is to produce diverse images in top ranking list covering various aspects of the query. In this context, we propose a Multi-view Concept-based Query Expansion (MVCQE) process, using a predefined list of semantic concepts and following three main steps. First, we harvest social knowledge to capture different contexts related to the query. Second, we perform a Multi-view Concepts weighting by applying concept-based query expansion for the initial query and for each of its contexts. Third, we select the most representative concepts using an adaptive threshold with respect to the dispersion of concept weights. Experiments using ambiguous queries over the NUS-WIDE dataset confirm the effectiveness of our process to improve the diversification compared to well known query expansion approaches. . .