Regularized Diffusion Process for Visual Retrieval
Authors: Song Bai, Xiang Bai, Qi Tian, Longin Jan Latecki
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the validity of the proposed Regularized Diffusion Process (RDP) with toy problems and real retrieval tasks. |
| Researcher Affiliation | Academia | Song Bai,1 Xiang Bai,1 Qi Tian,2 Longin Jan Latecki3 1Huazhong University of Science and Technology 2University of Texas at San Antonio, 3Temple University |
| Pseudocode | No | No pseudocode or algorithm block is explicitly presented in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology. |
| Open Datasets | Yes | Following the survey paper (Donoser and Bischof 2013), we assess the effectiveness of the proposed RDP on ORL face dataset, YALE face dataset B (Georghiades, Belhumeur, and Kriegman 2001) and MPEG-7 shape dataset (Latecki, Lak amper, and Eckhardt 2000). We also evaluate the proposed algorithm on the widely-used Ukbench dataset. |
| Dataset Splits | No | The paper describes using each image as a query and the rest as the database, but does not provide specific train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions using a 'pre-trained Alex Net' but does not specify software dependencies with version numbers. |
| Experiment Setup | Yes | The regularizer μ in Eq. (1) is set to 0.18. ... we set A(1) randomly and the iteration number to 100. ... graph sparsification is applied by only preserving edges within k nearest neighbors. ... For Face and Shape Retrieval, k = 5, k is set to 10. For Natural Image Retrieval, k is set to 4. |