Semantic Topic Multimodal Hashing for Cross-Media Retrieval
Authors: Di Wang, Xinbo Gao, Xiumei Wang, Lihuo He
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the proposed method outperforms several state-of-the-art methods. To evaluate the performance of the proposed STMH, we conduct comparison experiments with several state-of-art methods including CMSSH [Bronstein et al., 2010], CVH [Kumar and Udupa, 2011], LCMH [Zhu et al., 2013], IMH [Song et al., 2013], CMFH [Ding et al., 2014], and LSSH [Zhou et al., 2014], on two real-world datasets, i.e., Wiki1 and NUS-WIDE2 for cross-media similarity search. |
| Researcher Affiliation | Academia | Di Wang, Xinbo Gao, Xiumei Wang, Lihuo He School of Electronic Engineering, Xidian University Xi an, 710071, China wangdi.wandy@gmail.com, xbgao@mail.xidian.edu.cn, wangxm@xidian.edu.cn, lihuo.he@gmail.com |
| Pseudocode | Yes | Algorithm 1 Semantic Topic Multimodal Hashing Training: Input: Images X, texts Y, parameters λ, µ, and γ, bit length k. Output: Hash codes H, matrices F, U, and P. Procedure: 1. Initialize F, H, U, V, and P; 2. Repeat 2.1 Compute DX with Eq. (8); 2.2 Fix H, U, V, and P, update F with Eq. (12); 2.3 Fix F, H, V, and P, update U with Eq. (13); 2.4 Fix F, H, U, and V, update P with Eq. (14); 2.5 Fix F, H, U, and P, update V with Eq. (15); 2.6 Fix F, U, V, and P, update H by solving Eq. (17). until convergency. 3. Return H, F, U, and P. Testing: Input: Image xt or text yt, matrices F, U, and P. Output: Hash code ht. Procedure: Text: Get the hash code ht by solving Eq. (18). Image: Get the hash code ht with Eqs. (19) and (20). |
| Open Source Code | No | No explicit statement providing concrete access to the source code for the methodology described in this paper was found. The paper states 'For all the comparison algorithms except LCMH, the codes are kindly provided by the authors.' but not for STMH. |
| Open Datasets | Yes | on two real-world datasets, i.e., Wiki1 and NUS-WIDE2 for cross-media similarity search. 1http://www.svcl.ucsd.edu/projects/crossmodal/ 2http://lms.comp.nus.edu.sg/research/NUS-WIDE.htm |
| Dataset Splits | No | The paper mentions specific training and testing splits for both Wiki and NUS-WIDE datasets (e.g., '2173 pairs for training and 693 pairs for testing' for Wiki; '1K images with their tags to serve as the test set and the rest images and tags are serving as the training set in NUS-WIDE'). However, it does not explicitly specify a distinct validation split or its size/percentage, nor does it provide details on cross-validation beyond these train/test splits. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or cloud instance types) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | When comparing with the baseline methods, we use the parameter settings, λ = 0.5, µ = 0.001, and γ =10 4 for STMH. |