Robust discrete code modeling for supervised hashing.

•We propose a novel supervised hashing scheme to generate high-quality hash codes and hash functions for facilitating large-scale multimedia applications.•We devise an effective binary code modeling approach based on l2,p-norm, which can adaptively induce sample-wise sparsity, to perform automatic code selection as well as noisy samples identification.•We preserve the discrete constraint in the proposed model to directly produce discrete codes with minimal quantization error. An efficient algorithm is designed to solve the discrete optimization problem, where a weighted discrete cyclic coordinate decent (WDCC) algorithm is proposed to derive robust binary codes.•Extensive experiments conducted on various real-world datasets demonstrate the promising results of the RDCM approach in retrieval and classification tasks.