A Local Sparse Model for Matching Problem
Authors: Bo Jiang, Jin Tang, Chris Ding, Bin Luo
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Promising experimental results show the effectiveness of the proposed LSM method. In this section, we apply LSM to some matching tasks. We compare our LSM with the match selection method SM (Leordeanu and Hebert 2005), Game M (Albarelli et al. 2009), and Enet M (Rodol a et al. 2013). |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Anhui University, Hefei, 230601, China 2CSE Department, University of Texas at Arlington, Arlington, TX 76019, USA |
| Pseudocode | No | The paper describes an iterative update rule in equation form (Eq. 8) and prose in the 'Computational algorithm' section, but it does not present it as a structured pseudocode block or algorithm. |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology was found. |
| Open Datasets | Yes | Following the experimental setting (Cho, Lee, and Lee 2010; Leordeanu and Hebert 2005), we have randomly generated data sets of n M 2D model point set M. In this section, we perform feature matching on CMU and YORK sequences (Zhou and la Torre 2012; Cho, Lee, and Lee 2010; Luo and Hancock 2001). |
| Dataset Splits | No | No specific details about training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit mention of 'validation set') were provided, beyond describing how data was generated or selected for evaluation. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | Yes | The affinity matrix W is computed by Wij,kl = exp( r D ik r M jl 2 F /σr), where σr was set to 0.05 in this experiment, and r D ik is the Euclidean. Firstly, set threshold δt = 0.001 mean(X(t)). |