Semisupervised Hashing via Kernel Hyperplane Learning for Scalable Image Search

Abstract

Hashing methods that aim to seek a compact binary code for each image are demonstrated to be efficient for scalable content-based image retrieval. In this paper, we propose a new hashing method called semisupervised kernel hyperplane learning (SKHL) for semantic image retrieval by modeling each hashing function as a nonlinear kernel hyperplane constructed from an unlabeled dataset. Moreover, a Fisher-like criterion is proposed to learn the optimal kernel hyperplanes and hashing functions, using only weakly labeled training samples with side information. To further integrate different types of features, we also incorporate multiple kernel learning (MKL) into the proposed SKHL (called SKHL-MKL), leading to better hashing functions. Comprehensive experiments on CIFAR-100 and NUS-WIDE datasets demonstrate the effectiveness of our SKHL and SKHL-MKL.