A query analytic model for image retrieval

Searching digital images on a networked environment is rapidly growing. Despite recent advances in image retrieval technologies, high-precision and robust solutions remain hampered by limits to knowledge about user issues associated with image retrieval. This paper examines a large number of queries from a Web image search engine, and attempts to develop an analytic model to investigate their implications for image retrieval technologies. The model employs the concepts of uniqueness and refinement to categorize successful and failed queries. The results show that image requests have a higher specificity and may often contain queries refined by interpretive, reactive, and perceptual attributes. Based on the proposed model, the study further investigates feasible technical solutions integrating both content-based and concept-based technologies to deal with real image query types. The initial study has provided useful results that enhance the understanding of digital image searching and suggests implications for the improvement of image retrieval systems.