A semantic subspace learning method to exploit relevance feedback log data for image retrieval

Conventional content-based image retrieval (CBIR) systems with the Euclidean distance metric in a high-dimensional visual feature space usually cannot achieve satisfactory performance due to the semantic gap. Relevance feedback (RF) has been introduced as a powerful tool to involve the user in the system to improve the performance of CBIR. Despite the success, an on-line learning task can be tedious and boring for the user. Various schemes have been proposed to exploit the RF log data to further enhance the performance of CBIR. In this paper, we propose a semantic subspace learning (SSL) method to exploit the RF log data with contextual information for an image retrieval task. Different from conventional subspace learning approaches, our method can directly learn a semantic concept subspace from the RF log data with contextual information without using any class label information. We show that the performance of the image retrieval task can be significantly improved in the low-dimensional semantic concept subspace. Extensive experiments on a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of CBIR by exploiting the RF log data.