Many　fuzzy　subspace　clustering　methods　have　been　proposed　for　high-dimensional　image　data　with　rich　structural　information.　However,　since　these　methods　do　not　fully　exploit　the　subspace　information　in　each　cluster,　their　performance　on　image　clustering　is　still　not　promising.　In　this　work,　we　propose　to　find　soft　partitions　directly　based　on　the　construction　of　subspaces.　For　each　cluster,　we　use　a　bilinear　orthogonal　subspace　to　represent　it.　Then,　through　the　reconstruction　error　of　a　sample　in　the　subspace　corresponding　to　a　cluster,　a　new　membership　measure　for　the　sample　to　the　cluster　is　established.　Furthermore,　the　graph　regularization　is　imposed　on　these　bilinear　subspaces　to　preserve　the　local　relational　or　manifold　information　of　the　image　data　in　the　original　space.　Altogether,　we　get　a　clustering　model　considering　not　only　the　subspace　information　but　also　the　manifold　information　in　image　data.　An　efficient　optimization　algorithm　is　proposed　to　our　model,　and　its　theoretical　convergence　and　time　complexity　are　presented　correspondingly.　The　proposed　method　is　a　one-stage　clustering　model　that　does　not　require　vectorized　image　data,　thereby　reducing　the　computational　burden　while　maintaining　the　structural　relationship　between　pixels　in　the　image.　Competitive　experimental　results　on　benchmark　datasets　show　that　our　model　can　converge　quickly　with　strong　clustering　performance,　which　confirms　the　efficiency　and　superiority　of　the　proposed　method　compared　to　other　state-of-the-art　fuzzy　clustering　methods.