Locally Linear Metric Adaptation with Application to Image Retrieval

Many supervised and unsupervised learning algorithms are very sensitive to the choice of an appropriate distance metric. While classification tasks can make use of class label information for metric learning, such information is generally unavailable in conventional clustering tasks. Some recent research sought to address a variant of the conventional clustering problem called semi-supervised clustering, which performs clustering in the presence of some background knowledge or supervisory information expressed as pairwise similarity or dissimilarity constraints. However, existing metric learning methods for semi-supervised clustering mostly perform global metric learning through a linear transformation. In this paper, we propose a new metric learning method which performs nonlinear transformation globally but linear transformation locally. Experimental results for semi-supervised clustering demonstrate the effectiveness of locally linear metric adaptation (LLMA). Besides applying LLMA to semi-supervised learning, we have also used it to improve the performance of content-based image retrieval systems through metric adaptation. Experimental results based on two real-world image databases show that LLMA significantly outperforms other methods in improving the image retrieval performance.