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Table 6 The accuracy rates for the proposed method and the state-of-the-art algorithms (%)

From: Vehicle color classification using manifold learning methods from urban surveillance videos

Methods

Features

Classifiers

Average accuracy rates

Computational time (ms)

Baek [3]

H (36)*S (10)

SVM

73.88 (±1.0)

18

Kim [4]

H (8)*S (4)*I (4)

1-NN

71.04 (±1.12)

824

Yang [5]

Layer 1: H (16) + S (8)

Two-layer rule-based classifier

64.03 (±1.3)

34

Layer 2: normalized RGB

Hsieh [11]

Lab + transformed RGB

GMM + two-stage SVM

84.77 (±0.83)

58

Dule [21]

HS (64) + SV (64) + ab (64)

Neural network

76.12 (±1.41)

1,210

+La (64) + Lb (64) + Gray(8)

Wu [22]

HS (256) + HV (256) + SV (256)

Two-stage SVM

80.66 (±1.5)

33

The proposed method

Six color spaces (4,608)

NFL (20) + SVM (RBF-kernel function)

88.18 (±0.89)

18

PCA reduction (200)