Abstract:

The purpose of the manifold learning is to discover the intrinsic dimensions of nonlinear high-dimensional data, which makes it more suitable for data analysis and dimensional reduction. The gap between high-dimensional data space and low-dimensional semantic subspace forms the “semantic gap” problem in image retrieval. Although using relevance feedback mechanism can narrow down the gap and increase the retrieval accuracy, the limitations of relevance feedback and the high dimensionality of image features make it prone to the course of dimensionality. Manifold learning has brought promise for settling these problems. Using the learned intrinsic dimensions of highdimensional image feature data by manifold learning can considerably enhance retrieval performance. The image retrieval algorithms based on manifold learning all take semisupervised learning strategy. It makes the most of the feedback information to learn the semantic subspace of image, and reduces the high dimensionality effectively.

Key words: image retrieval, manifold learning, relevance feedback, dimension reduction