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LHSP (Lin and Hanrahan Similarity Palettes) Dataset

Dynamic Closest Color Warping to Sort and Compare Palettes
Suzi Kim and Sunghee Choi
Geometric Computing Lab., School of Computing, KAIST
Presented at ACM SIGGRAPH 2021

1. Description

  • LHSP is a dataset to measure the palette similarity.
  • For each query palette, we test how many sibling palettes are found.

2. Structure

2-1. Tree

/LHSP
├── /swatches
└── /LHSP_k%d_jitter%d_replacement%d
	├── query-palettes.csv
	├──/query-palettes-images
	│   ├── 0-{1st query palette' source image name}.png
	│   ├── 1-{2nd query palette' source image name}.png
	│   ├── ...
	│   └── n-{(n+1)-th palette's source image name}.png
	├── retrieval-palettes.csv
	└──/retrieval-palettes-images
		├── 0-{1st retrieval palette's source image name}.png
		├── 1-{2nd retrieval palette's source image name}.png
		├── ...
		└── m-{(m+1)-th retrieval palettes source image name}.png

/swatches

  • This includes original swatches from Lin and Hanrahan's. It is the set of the swatches that participants and artists can choose from.

/LHSP_k%d_jitter%d_replacement%d

parameter description
k length of palette
jitter jittering offset (alpha in RGB space)
replacement count of new colors replaced from the original swatches

2-2. Structure of each CSV file

query_palettes.csv

  • List of query palettes
  • Structure of each query:
    • {query's source image} {lenght of the query palette} {hex colors}

retrieval_palettes.csv

  • List of retrieval palettes
  • Structure of each target:
    • {target's source image} {lenght of the query palette} {hex colors}

3. Credits of base color schemes and swatches

4. Publication

Our paper is available at ACM Digital Library. Please cite with the following Bibtex code:

@article{kim2021dynamic,
    title = {Dynamic Closest Color Warping to Sort and Compare Palettes},
    author = {Kim, Suzi and Choi, Sunghee},
    year = {2021},
    journal = {ACM Transactions on Graphics (Proceedings SIGGRAPH)},
    volume = {40},
    number = {4},
    articleno = {95},
    address = {New York, NY, USA},
    doi = {10.1145/3450626.3459776},
}

5. Acknowledgements

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2019-0-01158, Development of a Framework for 3D Geometric Model Processing)