Robust discrete code modeling for supervised hashing
| Autor: | Heng Tao Shen, Fumin Shen, Zi Huang, Pan Zhou, Yang Yang, Yadan Luo |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Universal hashing Dynamic perfect hashing Hash function 020207 software engineering Pattern recognition 02 engineering and technology K-independent hashing Artificial Intelligence Discrete optimization Signal Processing 0202 electrical engineering electronic engineering information engineering Code (cryptography) 020201 artificial intelligence & image processing Binary code Computer Vision and Pattern Recognition Feature hashing Artificial intelligence business Algorithm Software Mathematics |
| Zdroj: | Pattern Recognition. 75:128-135 |
| ISSN: | 0031-3203 |
| Popis: | Recent years have witnessed the promising efficacy and efficiency of hashing (also known as binary code learning) for retrieving nearest neighbor in large-scale data collections. Particularly, with supervision knowledge (e.g., semantic labels), we may further gain considerable performance boost. Nevertheless, most existing supervised hashing schemes suffer from the following limitations: (1) severe quantization error caused by continuous relaxation of binary codes; (2) disturbance of unreliable codes in subsequent hash function learning; and (3) erroneous guidance derived from imprecise and incomplete semantic labels. In this work, we propose a novel supervised hashing approach, termed as Robust Discrete Code Modeling (RDCM), which directly learns high-quality discrete binary codes and hash functions by effectively suppressing the influence of unreliable binary codes and potentially noisily-labeled samples. RDCM employs l2, p norm, which is capable of inducing sample-wise sparsity, to jointly perform code selection and noisy sample identification. Moreover, we preserve the discrete constraint in RDCM to eliminate the quantization error. An efficient algorithm is developed to solve the discrete optimization problem. Extensive experiments conducted on various real-life datasets show the superiority of the proposed RDCM approach as compared to several state-of-the-art hashing methods. |
| Databáze: | OpenAIRE |
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