Learning Affine Robust Binary Codes Based on Locality Preserving Hash

Abstract

In large scale vision applications, high-dimensional descriptors extracted from image patches are in large quantities. Thus hashing methods that compact descriptors to binary codes have been proposed to achieve practical resource consumption. Among these methods, unsupervised hashing aims to preserve Euclidean distances, which do not correlate well with the similarity of image patches. Supervised hashing methods exploit labeled data to learn binary codes based on visual or semantic similarity, which are usually slow to train and consider global structure of data. When data lie on a sub-manifold, global structure can not reflect the inherent structure well and may lead to incompact codes. We propose locality preserving hash (LPH) to learn affine robust binary codes. LPH preserves local structure by embedding data into a sub-manifold, and performing binarization that minimize false classification ratio while keeping partition balanced. Experiments on two datasets show that LPH is easy to train and performs better than state-of-the-art methods with more compact binary codes.