Image analysis using new set of separable two-dimensional discrete orthogonal moments based on Racah polynomials

EURASIP Journal on Image and Video Processing

Table 5 Image classification rate (%) using GMI, RTMI, RKMI, RdHMI, RRMI, TTMI, KKMI, and dHdHMI

Number of classes	Outex			Caltech-101			Corel
	4	6	10	4	6	10	4	6	10	Mean
GMI	76.11	67.87	62.56	78.56	68.64	54.89	77.5	52.83	39.7	64.30
RTMI	79.44	78.61	68.83	85.62	71.42	59.78	89.25	76.32	47.10	72.93
RKMI	79.72	79.35	70.33	86.15	73.17	60.52	89.50	77.60	48.20	73.84
RdHMI	79.26	79.17	69.83	85.08	71.89	59.69	89.00	75.17	48.20	73.03
RRMI	75.69	66.57	58.72	81.49	70.61	59.13	89.25	77.5	55.7	70.52
TTMI	75.14	58.8	57.83	82.02	69.57	58.27	88.75	73.5	47.0	67.88
KKMI	77.64	59.17	58.5	82.15	68.75	57.2	89.25	74.17	48.6	68.38
dHdHMI	78.89	61.94	57.33	80.16	70.27	58.67	88.25	76.67	46.3	68.72

The data in italic present the performance of our proposed methods in the image classification