4. The process of claim 3 , wherein, given a set of structured metadata comprising a set of training shots x i and related instance classification labels y, to the target concepts for x i , wherein, x i and y i are given by the equation {(x i ,y i ) i=1 N 1 , and N 1 is the total number of training shots x i , and the set of training shots x i comprises a set I of sequentially indexed nodes given by the equation I ⁢ : ⁢ { x 1 , … ⁢ , x N 1 , x 11 2 , … ⁢ , x N 1 ⁢ N N 1 1 2 , x 11 3 , … ⁢ , x N 1 ⁢ N N 1 3 3 } , wherein x im l is an m th sub-structure in the l th layer for shot x i , and N i l is the number of sub-structures in the l th layer for shot x i , the modified learned objective decision function f( ) for the particular shot x is given by the equation f(x)=k MLMI (x i ,x)Aα+b, wherein, α is a vector of prescribed Lagrange multiplier coefficients given by the equation α=[α 1 1 , . . . , α N 1 1 , α 1 2 , . . . ,α N 1 2 , α 1 3 , . . . , α N 1 3 ] T , k MLMI (x i ,x) is the MLMI kernel which compares the training shots x i to x, A is a multi-instance transform matrix given by the equation A IJ = { y I I , J ∈ [ 1 , N 1 ] - 1 I ∈ [ 1 , N 1 ] , J ∈ [ ( 2 ⁢ l - 3 ) ⁢ N 1 + 1 , ( 2 ⁢ l - 2 ) ⁢ N 1 ] ; 2 ≤ l ≤ 3 1 I ∈ [ 1 , N 1 ] , J ∈ [ ( 2 ⁢ l - 2 ) ⁢ N 1 + 1 , ( 2 ⁢ l - 1 ) ⁢ N 1 ] ; 2 ≤ l ≤ 3 β I I ∉ [ 1 , N 1 ] , J ∈ [ ( 2 ⁢ l - 3 ) ⁢ N 1 + 1 , ( 2 ⁢ l - 2 ) ⁢ N 1 ] ; l ⁢ ⁢ is ⁢ ⁢ the ⁢ ⁢ layer ⁢ ⁢ of ⁢ ⁢ node ⁢ ⁢ set ⁢ ⁢ I ⁡ ( I ) - β I I ∉ [ 1 , N 1 ] , J ∈ [ ( 2 ⁢ l - 2 ) ⁢ N 1 + 1 , ( 2 ⁢ l - 1 ) ⁢ N 1 ] ; l ⁢ ⁢ is ⁢ ⁢ the ⁢ ⁢ layer ⁢ ⁢ of ⁢ ⁢ node ⁢ ⁢ set ⁢ ⁢ I ⁡ ( I ) 0 otherwise , β I is a prescribed coefficient for node set I (I) on layer l, and b is a prescribed bias coefficient.
4. The process of claim 3 , wherein, given a set of structured metadata comprising a set of training shots x i and related instance classification labels y, to the target concepts for x i , wherein, x i and y i are given by the equation {(x i ,y i ) i=1 N 1 , and N 1 is the total number of training shots x i , and the set of training shots x i comprises a set I of sequentially indexed nodes given by the equation I ⁢ : ⁢ { x 1 , … ⁢ , x N 1 , x 11 2 , … ⁢ , x N 1 ⁢ N N 1 1 2 , x 11 3 , … ⁢ , x N 1 ⁢ N N 1 3 3 } , wherein x im l is an m th sub-structure in the l th layer for shot x i , and N i l is the number of sub-structures in the l th layer for shot x i , the modified learned objective decision function f( ) for the particular shot x is given by the equation f(x)=k MLMI (x i ,x)Aα+b, wherein, α is a vector of prescribed Lagrange multiplier coefficients given by the equation α=[α 1 1 , . . . , α N 1 1 , α 1 2 , . . . ,α N 1 2 , α 1 3 , . . . , α N 1 3 ] T , k MLMI (x i ,x) is the MLMI kernel which compares the training shots x i to x, A is a multi-instance transform matrix given by the equation A IJ = { y I I , J ∈ [ 1 , N 1 ] - 1 I ∈ [ 1 , N 1 ] , J ∈ [ ( 2 ⁢ l - 3 ) ⁢ N 1 + 1 , ( 2 ⁢ l - 2 ) ⁢ N 1 ] ; 2 ≤ l ≤ 3 1 I ∈ [ 1 , N 1 ] , J ∈ [ ( 2 ⁢ l - 2 ) ⁢ N 1 + 1 , ( 2 ⁢ l - 1 ) ⁢ N 1 ] ; 2 ≤ l ≤ 3 β I I ∉ [ 1 , N 1 ] , J ∈ [ ( 2 ⁢ l - 3 ) ⁢ N 1 + 1 , ( 2 ⁢ l - 2 ) ⁢ N 1 ] ; l ⁢ ⁢ is ⁢ ⁢ the ⁢ ⁢ layer ⁢ ⁢ of ⁢ ⁢ node ⁢ ⁢ set ⁢ ⁢ I ⁡ ( I ) - β I I ∉ [ 1 , N 1 ] , J ∈ [ ( 2 ⁢ l - 2 ) ⁢ N 1 + 1 , ( 2 ⁢ l - 1 ) ⁢ N 1 ] ; l ⁢ ⁢ is ⁢ ⁢ the ⁢ ⁢ layer ⁢ ⁢ of ⁢ ⁢ node ⁢ ⁢ set ⁢ ⁢ I ⁡ ( I ) 0 otherwise , β I is a prescribed coefficient for node set I (I) on layer l, and b is a prescribed bias coefficient. 5. The process of claim 4 , wherein, constraint A is a Hinge loss function which measures disagreement between the instance classification labels y i and classification of the shots x i in the shot layer, constraint B is a Hinge loss function which measures disagreement between the instance classification labels y i classification of the shots x i in the key-frame sub-layer and the key-region sub-layer, and constraint C is an inter-layer inconsistency loss function which measures disagreement between classification of the shots x i in the shot layer, the key-frame sub-layer and the key-region sub-layer, wherein, an L1 loss function is employed for constraints A, B and C.