| Autor: |
Endoh, Megumi, Yanagimachi, Tomohiro, Ohbuchi, Ryutarou |
| Zdroj: |
2012 IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP); 1/ 1/2012, p2345-2348, 4p |
| Abstrakt: |
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. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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