Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Authors: Heinrich Jiang, Jennifer Jang, Samory Kpotufe
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then show strong clustering performance on real datasets as well as promising applications to image segmentation.Despite the simplicity of our approach, we show that Quickshift++ considerably outperforms the popular density-based clustering algorithms, while being efficient.5. Simulations, 6. Image Segmentation, 7. Clustering Experiments |
| Researcher Affiliation | Collaboration | 1Google Research, Mountain View, CA 2Uber Inc, San Francisco, CA 3Princeton University, Princeton, NJ. |
| Pseudocode | Yes | Algorithm 1 MCores (estimating cluster-cores) and Algorithm 2 Quickshift++ |
| Open Source Code | Yes | Code release is at https://github.com/google/quickshift. |
| Open Datasets | Yes | Datasets Used: Summary of the datasets can be found in Figure 8. Seeds, glass, and iris are standard UCI datasets (Lichman, 2013) used for clustering... We also used a small subset of MNIST (Le Cun et al., 2010) for our experiments. |
| Dataset Splits | No | The paper runs experiments on various real datasets and scores against groundtruth, but it does not explicitly provide details about train/validation/test splits, percentages, or sample counts for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or other system specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions general software like 'scikit-learn' but does not provide specific version numbers for any key software components or libraries required to replicate the experiments. |
| Experiment Setup | Yes | Quickshift++ settings were fixed at k = 20, β = 0.7 for all the datasets (Figure 3). β = 0.9 is reasonable across a wide range of images, β was fixed to this value for segmentation here. (Section 6). Quick Shift was set with bandwidth 10 and Quickshift++ was set with k = 300 and β = 0.9. (Figure 4 caption). β = 0.3 for all but one of the datasets (Section 7). The procedures were tuned in their respective essential hyperparameter: k-means number of clusters, DBSCAN epsilon, MCores k, mean shift bandwidth, quick shift bandwidth, Quickshift++ k. (Figure 9 caption) |