Reducing Impact of Inaccurate User Feedback in Face Retrieval

Abstract

A main problem in face retrieval is the semantic gap between low-level features and high-level semantic concepts. Relevance feedback (RF) may be used to incorporate to reduce the semantic gap. However, in the search for a specific target in a facial image database, a user's assignment of RF instances may be mistaken. This would make the system prediction of the user's target in a wrong way. Addressing this problem, we propose a new query point movement technique for target search by posing the problem of reducing the impact of inaccurate user feedback as an optimization problem. We develop a support vector machine based method to learn a decision boundary to identify ideal irrelevant images. Then we propose a rank function for finding target images, which would assign high scores to the images near the relevant images and punish those close to the decision boundary. Experiments are performed to show the stability and efficiency of the proposed algorithm.