Table 6 Comparison of model accuracy by artist (unit: %)

From: Toward enhanced unsupervised clustering of 20th century Korean paintings via multimodal features

Dataset

Method

RGB

HSV

Histogram

LBP

GLCM

Ours

Kim Ki Chang

41.0

44.0

33.0

34.0

42.0

73.5

Kim Whan Ki

72.0

75.0

41.0

66.0

76.0

74.8

To Sang Bong

98.0

98.0

37.0

98.0

100

97.0

Park Soo Keun

56.0

56.0

38.0

50.0

56.0

69.1

Byeon Gwan Sik

50.0

49.0

30.0

53.0

51.0

76.0

Byun Jong Ha

49.0

58.0

24.0

36.0

51.0

78.3

Yoo Young Kuk

85.0

92.0

56.0

70.0

93.0

94.9

Lee Sang Beom

65.0

64.0

32.0

63.0

60.0

78.4

Lee Jung Seop

43.0

42.0

27.0

26.0

40.0

74.4

Chang Uc Chin

49.0

47.0

24.0

36.0

60.0

86.4

Cheon Gyeong Ja

67.7

75.8

18.2

65.7

74.8

82.3

  1. All methods use as the backbone; labels denote additional features concatenated to the CLIP embedding. The bold values indicate the best-performing results for each comparison setting, representing the highest clustering or classification accuracy achieved among the evaluated methods or feature combinations.