Multiple object retrieval for image databases using multiple instance learning and relevance feedback
Chengcui Zhang, Shu‐Ching Chen, Mei‐Ling Shyu · 2005
The paper proposes a method to discover effectively users' concept patterns when multiple objects of interest (e.g., foreground and background objects) are involved in content-based image retrieval. The proposed method incorporates multiple instance learning into the user relevance feedback in a seamless way to discover where the user's objects/regions of most interest are and how to map the local features of that(those) region(s) to the user's high-level concepts. A three-layer neural network is used to model the underlying mapping progressively through the feedback and learning procedure.