Application of Artificial Bee Colony Optimization Algorithm for Image Classification Using Color and Texture Feature Similarity Fusion

Table 1

Mean and variance of texture feature derived using cooccurrence matrix for contrast, energy, entropy, correlation, and local stationary in four orientations.

Orientations	Sl. no.	Bus (301.jpg)		Dinosaur (421.jpg)		Elephant (517.jpg)		Horse (726.jpg)
		Mean	Variance	Mean	Variance	Mean	Variance	Mean	Variance
0°	1	0.1283	0	0.9022	0	0.2214	0	0.6007	0
	2	0.8867	0	0.0982	0	0.7918	0	0.4053	0
	3	0.5099	0	0.2811	0	0.4971	0	0.6068	0
	4	1.0000	0	0.0982	0.0102	0.8220	0.0006	0.4096	0.0125
	5	0	0	0	0.0004	0.0087	0.0001	0	0.0003
45°	6	0.0367	0	0.3508	0	0.0497	0	0.5000	0
	7	1.9600	0	1.3200	0	1.9333	0	2.0000	0
	8	0.1414	0	0.9248	0	0.2108	0	0	0
	9	1.0000	0	1.0000	0	1.0000	0	0	0
	10	0	0	0	0	0	0	0	0
90°	11	0.0367	0	0.1872	0	0.5000	0	0.5000	0
	12	2.9400	0	2.4800	0	3.0000	0	3.0000	0
	13	0.1414	0	0.6653	0	0	0	0	0
	14	1.0000	0	1.0000	0	0	0	0	0
	15	0	0	0	0	0	0	0	0
135°	16	0.9689	0	0.0694	0	0.3306	0	0.9928	0
	17	0.1209	0	3.7867	0	2.7221	0	0.0235	0
	18	0.1030	0	0.3004	0	0.6795	0	0.0241	0
	19	0.0178	0.0016	1.0000	0	0.7744	0.0012	0.0016	0.0001
	20	0.0060	0.0478	0	0	−0.0206	0.0066	0	0.0129