Semi-supervised dimensionality reduction in image feature space

Hao Cheng, Kien A. Hua, Khanh Vu, Danzhou Liu

Abstract: Image feature space is typically complex due to the high dimensionality of data. Effective handling of this space has prompted many research efforts in the study of dimensionality reduction in the image domain. In this paper, we propose a semi-supervised reduction method that leverages relevance feedback information in the retrieval process to learn suitable linear and orthogonal embeddings. In the reduced space constructed by the proposed embedding, relevant images are kept close to each other, while irrelevant ones are dispersed far apart. The experimental results demonstrate the superiority of our method.