Content-based image search system design for capturing user preferences during query formulation
Year: 2020
Type: conference-paper
Abstract: Most existing studies of content-based image retrieval (CBIR) system design focus on learning users’ information needs through relevance feedback at the result assessment stage only. However, in many CBIR systems, the underlying machine learning mechanisms need the users’ feedback at query formulation stage for a better training and search performance, which unfortunately is often not supported by the search interface design. The lack of support for the users’ query formulation through an effective CBIR interface has been a drawback for system performance and the users’ search satisfaction and experiences. We propose a new CBIR system design approach based on Vakkari’s three-stage model, which encourages the users to provide feedback at the query formulation stage through a user-centered interface. The interface helps the users to form and express their information needs through enabling the users to participate in the training phase of the machine learning mechanism of the system. A user study with 28 participants shows how the proposed system design supports the users’ interaction through the user-centered search interface. The findings of this study highlight the importance for the users to engage in all stages of the search process, especially at the query formulation stage when the considered mechanism requires a training process, through a user-centered interaction design.
Authors: Mahmoud Artemi, Haiming Liu
Institution: University of Bedfordshire
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3109329133