Query feedback for interactive image retrieval

From a perceptual standpoint, the subjectivity inherent in understanding and interpreting visual content in multimedia indexing and retrieval motivates the need for online interactive learning. Since efficiency and speed are important factors in interactive visual content retrieval, most of the current approaches impose restrictive assumptions on similarity calculation and learning algorithms. Specifically, content-based image retrieval techniques generally assume that perceptually similar images are situated close to each other within a connected region of a given space of visual features. This paper proposes a novel method for interactive image retrieval using query feedback. Query feedback learns the user query as well as the correspondence between high-level user concepts and their low-level machine representation by performing retrievals according to multiple queries supplied by the user during the course of a retrieval session. The results presented in this paper demonstrate that this algorithm provides accurate retrieval results with acceptable interaction speed compared to existing methods.