Visual query attributes suggestion

Abstract

Query suggestion is an effective solution to help users deliver their search intent. While many query suggestion approaches have been proposed for test-based image retrieval with query-by-keywords, query suggestion for content-based image retrieval (CBIR) with query-by-example (QBE) has been seldom studied. QBE usually suffers from the "intention gap" problem, especially when the user fails to get an appropriate query image to express his search intention precisely. In this paper, we propose a novel query suggestion scheme named Visual Query Attributes Suggestion (VQAS) for image search with QBE. Given a query image, informative attributes are suggested to the user as complements to the query. These attributes reflect the visual properties and key components of the query. By selecting some suggested attributes, the user can provide more precise search intent which is not captured by the query image. The evaluation results on two real-world image datasets show the effectiveness of VQAS in terms of retrieval performance and the quality of query suggestions.