Unsupervised adaptive hashing based on feature clustering

Abstract

Abstract An attractive method for image retrieval is binary hashing, which aims to reduce the dimensionality and generate similarity-preserving binary codes. To map the high-dimensional data into a low-dimensional subspace, majority of current unsupervised hashing approaches reduce the dimensionality by principal component analysis (PCA). However, PCA will yield unbalanced variances of projection directions and cause inconvenience in the quantization step. Besides, preserving the original similarity in existing unsupervised hashing methods remains as an NP-hard problem. For addressing these problems, we explore a novel hashing method based on feature clustering to simultaneously generate low-dimensional data with balanced variance and preserve the similarity in Euclidean space. Furthermore, we also propose an adaptive quantization approach to displace the fixed threshold quantization. Our novel method, dubbed as Feature Clustering Hashing (FCH), has shown its superiority to state-of-the-art methods on three benchmark datasets.