Dynamic Multi-View Hashing for Online Image Retrieval
Authors: Liang Xie, Jialie Shen, Jungong Han, Lei Zhu, Ling Shao
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods. |
| Researcher Affiliation | Academia | 1Wuhan University of Technology, China 2Northumbria University, United Kingdom 3Lancaster University, United Kingdom 4The University of Queensland, Australia 5University of East Anglia, United Kingdom |
| Pseudocode | Yes | Algorithm 1 Online learning process of DMVH at step t. Input: xm t |M m=1,Dm t 1|M m=1, e Km t 1|M m=1, α Output: Ht, Dm t |M m=1, e Km t |M m=1, α 1: Compute ht by Eq.(5); 2: Compute lt(ht, Dm t 1|M m=1) by Eq.(6) 3: if lt(ht, Dm t 1|M m=1) < δ then 4: Ht = [HT t 1, sgn(h T t )]T; 5: e Km t = e Km t 1 and Dm t = Dm t 1; 6: else if lt(ht, Dm t 1|M m=1) >= δ then 7: Add t into the buffer, and use Algorithm 2 to optimize Ht, α and e Km t; 8: end if |
| Open Source Code | No | The paper does not provide any specific repository link or explicit statement about the availability of the source code for the described methodology. |
| Open Datasets | Yes | We use two multi-view image datasets: MIR Flickr [Huiskes and Lew, 2008] and NUS-WIDE [Chua et al., 2009] |
| Dataset Splits | No | The paper states 'We select 1% images as queries, and the rest images are added to the database sequentially.' for MIR Flickr and NUS-WIDE, which defines the query set but does not provide explicit training, validation, and test splits with percentages or counts, nor does it refer to predefined standard splits for these datasets. |
| Hardware Specification | Yes | All the experiments are conducted on a computer with Intel Core(TM) i5 2.6GHz 2 processors and 12.0GB RAM. |
| Software Dependencies | No | The paper mentions mathematical functions and parameters but does not provide specific software dependencies or library versions (e.g., Python, PyTorch, scikit-learn versions) required to replicate the experiment. |
| Experiment Setup | Yes | In the implementation of DMVH, we use Gaussian kernel for all visual features, and histogram intersection kernel for text feature. DMVH does not contain many parameters to set. The regularization λ is set to 10-3, it is used to avoid the matrix singularity and has little influence on the results. The maximum buffer size is set to 1000 on MIR Flickr and 5000 on NUS-WIDE respectively, the threshold ρ is set to 0.5. |