Efficient manifold learning for 3D model retrieval by using clustering-based training sample reduction

Retrieval accuracy in content-based multimedia retrieval can be improved by using distance metric learned from distribution of features in input feature space. One way to achieve this is by dimension reduction via manifold-learning, such as Locally Linear Embedding [8]. While effective in improving retrieval accuracy, these algorithms have high computational cost that depends on feature dimensionality d and number of training samples N. In this paper, we explore a clustering-based approach to reduce number of training samples; it uses L cluster centers (L≪N) computed from N input features as training samples. We propose to use extremely randomized clustering tree [3] for clustering. Experiments showed that the proposed approach produces better retrieval performance than random sampling, and that the randomized tree is much faster than the k-means algorithm.