Target　searching,　i.e.　fast　locating　target　objects　in　images　or　videos,　has　attracted　much　attention　in　computer　vision.　A　comprehensive　understanding　of　factors　influencing　human　visual　searching　is　essential　to　design　target　searching　algorithms　for　computer　vision　systems.　In　this　paper,　we　propose　a　combined　model　to　generate　scan　paths　for　computer　vision　to　follow　to　search　targets　in　images.　The　model　explores　and　integrates　three　factors　influencing　human　vision　searching,　top-down　target　information,　spatial　context　and　bottom-up　visual　saliency,　respectively.　The　effectiveness　of　the　combined　model　is　evaluated　by　comparing　the　generated　scan　paths　with　human　vision　fixation　sequences　to　locate　targets　in　the　same　images.　The　evaluation　strategy　is　also　used　to　learn　the　optimal　weighting　coefficients　of　the　factors　through　linear　search.　In　the　meanwhile,　the　performances　of　every　single　one　of　the　factors　and　their　arbitrary　combinations　are　examined.　Through　plenty　of　experiments,　we　prove　that　the　top-down　target　information　is　the　most　important　factor　influencing　the　accuracy　of　target　searching.　The　effects　from　the　bottom-up　visual　saliency　are　limited.　Any　combinations　of　the　three　factors　have　better　performances　than　each　single　component　factor.　The　scan　paths　obtained　by　the　proposed　model　are　optimal,　since　they　are　most　similar　to　the　human　vision　fixation　sequences.