Discrete Cross-Modal Hashing for Efficient Multimedia Retrieval
| Autor: | Ying Li, Ran He, Xiangwei Kong, Jian Liang, Dekui Ma |
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| Rok vydání: | 2016 |
| Předmět: |
Theoretical computer science
Universal hashing Computer science Dynamic perfect hashing 02 engineering and technology 010501 environmental sciences Linear hashing 01 natural sciences Hash table Locality-sensitive hashing Hopscotch hashing K-independent hashing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Feature hashing 0105 earth and related environmental sciences |
| Zdroj: | ISM |
| Popis: | Hashing techniques have been widely adopted for cross-modal retrieval due to its low storage cost and fast query speed. Most existing cross-modal hashing methods aim to map heterogeneous data into the common low-dimensional hamming space and then threshold to obtain binary codes by relaxing the discrete constraint. However, this independent relaxation step also brings quantization errors, resulting in poor retrieval performances. Other cross-modal hashing methods try to directly optimize the challenging objective function with discrete binary constraints. Inspired by [1], we propose a novel supervised cross-modal hashing method called Discrete Cross-Modal Hashing (DCMH) to learn the discrete binary codes without relaxing them. DCMH is formulated through reconstructing the semantic similarity matrix and learning binary codes as ideal features for classification. Furthermore, DCMH alternately updates binary codes of each modality, and iteratively learns the discrete hashing codes bit by bit efficiently, which is quite promising for large-scale datasets. Extensive empirical results on three real-world datasets show that DCMH outperforms the baseline approaches significantly. |
| Databáze: | OpenAIRE |
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