Locality Preserving Matching
Authors: Jiayi Ma, Ji Zhao, Hanqi Guo, Junjun Jiang, Huabing Zhou, Yuan Gao
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various real image pairs for general feature matching, as well as for visual homing and image retrieval demonstrate the generality of our method for handling different types of image deformations, and it is more than two orders of magnitude faster than state-of-the-art methods in the same range of or better accuracy. |
| Researcher Affiliation | Collaboration | Electronic Information School, Wuhan University, Wuhan 430072, China School of Computer Science, China University of Geosciences, Wuhan 430074, China School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430073, China Tencent AI Laboratory, Shenzhen 518057, China |
| Pseudocode | Yes | We summarize our LPM in Alg. 1. |
| Open Source Code | No | The paper mentions the use of 'the open source VLFEAT toolbox', which is a third-party tool, but does not provide a link or statement about the availability of their own source code for the described methodology. |
| Open Datasets | Yes | To test the computational efficiency of our LPM, we next conduct experiments on a publicly available feature matching dataset [Mikolajczyk et al., 2005]... We conduct experiments on the A1original H and CHall1H which are two scenes from a widely used panoramic image database for visual homing1. The two scenes contain 170 and 200 images, respectively... We also test our LPM for near-duplicate image retrieval and compare it with RANSAC, ICF, GS, and MR-RPM on the California-ND dataset [Jinda-Apiraksa et al., 2013]. |
| Dataset Splits | No | The paper mentions using publicly available datasets for evaluation but does not specify the training, validation, or test dataset splits (e.g., percentages or sample counts) used for reproducibility. |
| Hardware Specification | Yes | The experiments are performed on a desktop with 3.0 GHz Intel Core CPU, 8 GB memory, and C++ code. |
| Software Dependencies | No | The paper mentions 'the open source VLFEAT toolbox' and 'C++ code' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Parameter settings. There are two parameters in our method: K and λ. The former determines the number of nearest neighbors for neighborhood construction, while the latter controls the threshold for judging the correctness of a putative correspondence... In our evaluate, we set the default values as K = 4, and λ = 6. |