<title>Feature-adaptive relevance feedback (FA-RF) for content-based image retrieval</title>

Abstract

The paper proposes an adaptive retrieval approach based on the concept of relevance-feedback. The proposed approach establishes a link between high-level concepts and low-level features, using the user's feedback not only to assign proper weights to the features, but also to dynamically select them within a large collection of parameters. The target is to identify the set of relevant features according to a user query, maintaining at the same time a small sized feature vector to attain better matching and lower complexity. To this end, after each feedback the image description is modified by removing less significant features and better specifying most significant ones. The weights are then adjusted based on relevant and irrelevant output images without further requiring the user intervention. Results achieved on a large set of synthetic and natural data show that the proposed algorithm outperforms previously proposed methods.

Query Initialization by Virtual Query Image and Pre-initial Query Feedback

“…The goal is to adjust iteratively system's parameters in order to achieve a better approximation of the user's information needs. To further improve the adaptation capabilities, we proposed in the past a Feature-Adaptive Relevance Feedback method (FA-RF), which is based on the concept of dynamic feature spaces [16]. In FA-RF, the feedback is used not only to refine the query and to assign proper weights to the features, but also to dynamically select them within a large collection of parameters.…”