An empirical investigation of user term feedback in text-based targeted image search
Joyce Yue Chai, Chen Zhang, Rong Jin · ACM Transactions on Information Systems · 2007
Text queries are natural and intuitive for users to describe their information needs. However, text-based image retrieval faces many challenges. Traditional text retrieval techniques on image descriptions have not been very successful. This is mainly due to the inconsistent textual descriptions and the discrepancies between user queries and terms in the descriptions. To investigate strategies to alleviate this vocabulary problem, this article examines the role of user term feedback in targeted image search that is based on text-based image retrieval. Term feedback refers to the feedback from a user on specific terms regarding their relevance to a target image. Previous studies have indicated the effectiveness of term feedback in interactive text retrieval. However, in our experiments on text-based image retrieval, the term feedback has not been shown to be effective. Our results indicate that, although term feedback has a positive effect by allowing users to identify more relevant terms, it also has a strong negative effect by providing more opportunities for users to specify irrelevant terms. To understand these different effects and their implications, this article further analyzes important factors that contribute to the utility of term feedback and discusses the outlook of term feedback in interactive text-based image retrieval.