Optimal Projection Guided Transfer Hashing for Image Retrieval
Authors: Ji Liu, Lei Zhang8754-8761
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various benchmark databases verify that our method outperforms many state-of-the-art learning to hash methods. The implementation details are available at https://github.com/liuji93/GTH. |
| Researcher Affiliation | Academia | Ji Liu, Lei Zhang School of Microelectronics and Communication Engineering, Chongqing University, China jiliu@cqu.edu.cn, leizhang@cqu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Optimal Projection Guided Transfer Hashing |
| Open Source Code | Yes | The implementation details are available at https://github.com/liuji93/GTH. |
| Open Datasets | Yes | We perform the experiments on three groups benchmark datasets: PIE-C29&PIE-C05 from PIE (Sim, Baker, and Bsat 2002), Amazon&Dslr from Office (Saenko et al. 2010), and VOC2007&Caltech101 from VLCS (Torralba and Efros 2011). |
| Dataset Splits | No | The paper specifies training and testing sets, but does not explicitly mention a separate validation set or its size/percentage for model selection. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | For our GTH, we empirically set λ1 to 0.1 and λ2 to 1. The code length is set to 32. ... where τ denotes the step size. We empirically set τ = 0.1. |