Saliency Detection Using Sparse and Nonlinear Feature Representation

Table 4

Comparison of AUC and PoDSC score of the proposed model with other state-of-the-art models.

Algorithm	C1		C2		C3		C4		C5		C6		Avg.
	AUC	PoDSC	AUC	PoDSC	AUC	PoDSC	AUC	PoDSC	AUC	PoDSC	AUC	PoDSC	Avg. AUC
AIM	0.921	0.653	0.918	0.543	0.957	0.461	0.904	0.435	0.924	0.508	0.941	0.671	0.927
MESR	0.798	0.494	0.854	0.436	0.927	0.377	0.718	0.238	0.837	0.398	0.918	0.617	0.842
ICL	0.928	0.681	0.907	0.501	0.909	0.348	0.926	0.537	0.902	0.498	0.914	0.569	0.914
Itti	0.939	0.687	0.924	0.542	0.952	0.457	0.882	0.423	0.924	0.499	0.941	0.631	0.927
MPQFT	0.805	0.497	0.865	0.452	0.927	0.386	0.735	0.248	0.852	0.436	0.921	0.630	0.851
SDSR	0.879	0.618	0.908	0.511	0.948	0.435	0.801	0.298	0.904	0.489	0.933	0.645	0.896
SUN	0.766	0.459	0.787	0.349	0.880	0.362	0.708	0.263	0.719	0.283	0.817	0.451	0.780
HFT	0.937	0.700	0.923	0.552	0.937	0.448	0.964	0.648	0.933	0.554	0.960	0.706	0.942
LG	0.814	0.515	0.859	0.464	0.904	0.379	0.635	0.145	0.862	0.499	0.884	0.572	0.826
ERDEM	0.882	0.617	0.912	0.555	0.962	0.526	0.840	0.349	0.923	0.575	0.916	0.635	0.906
MQDCT	0.840	0.564	0.894	0.517	0.947	0.489	0.806	0.311	0.888	0.524	0.923	0.670	0.883
AWS	0.894	0.625	0.922	0.569	0.957	0.480	0.891	0.402	0.920	0.534	0.925	0.655	0.918
Proposed	0.920	0.653	0.943	0.596	0.976	0.555	0.934	0.516	0.942	0.532	0.956	0.688	0.945

The top ranked model is in bold font and the 2nd ranked model is in italic font. The overall best average AUC score is given by our proposed model.