Efficient manifold larning for 3D model retrieval by using clustering-based training sample reduction, poster paper
Efficient manifold larning for 3D model retrieval by using clustering-based training sample reduction, poster paper
复制标题
使用基于聚类的训练样本缩减进行 3D 模型检索的高效流形学习，海报论文
DOI：
10.1109/icassp.2012.6288385
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Ryutarou Ohbuchi
中科院分区：
文献类型：
--
作者：
Megumi Endoh;Tomohiro Yanagimachi;Ryutarou Ohbuchi
在基于内容的多媒体检索中，利用特征在输入特征空间中的分布来学习距离度量，可以提高检索精度。实现这一点的一种方法是通过流形学习进行降维，例如局部线性嵌入[8]。虽然这些算法在提高检索精度方面是有效的，但这些算法具有较高的计算成本，这取决于特征维数d和训练样本的数量N。在本文中，我们探索了一种基于聚类的方法来减少训练样本的数量;它使用从N个输入特征计算的L个聚类中心（L N）作为训练样本。我们建议使用极端随机聚类树[3]进行聚类。实验表明，该方法比随机抽样具有更好的检索性能，并且随机树比k-means算法快得多。
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.