An analysis of failed queries for web image retrieval

Abstract

This paper examines a large number of failed queries submitted to a web image search engine, including real users' search terms and written requests. The results show that failed image queries have a much higher specificity than successful queries because users often employ various refined types to specify their queries. The study explores the refined types further, and finds that failed queries consist of far more conceptual than perceptual refined types. The widely used content-based image retrieval technique, CBIR, can only deal with a small proportion of failed queries; hence, appropriate integration of concept-based techniques is desirable. Based on using the concepts of uniqueness and refinement for categorization, the study also provides a useful discussion on the gaps between image queries and retrieval techniques. The initial results enhance the understanding of failed queries and suggest possible ways to improve image retrieval systems.