5. The process of claim 1 , wherein the semantics of each training image are assumed to be represented by the vector of keyword annotations for said image, and the process action of partitioning into a plurality of semantic clusters of training images comprises actions of: computing a pair-wise annotation-based semantic distance score SD( ) between every possible pair of training images in wherein each score SD( ) specifies a metric for the intuitive semantic distance between a particular pair of training images in and utilizing said scores SD( ) to partition the training images in into H different semantic clusters of training images.
5. The process of claim 1 , wherein the semantics of each training image are assumed to be represented by the vector of keyword annotations for said image, and the process action of partitioning into a plurality of semantic clusters of training images comprises actions of: computing a pair-wise annotation-based semantic distance score SD( ) between every possible pair of training images in wherein each score SD( ) specifies a metric for the intuitive semantic distance between a particular pair of training images in and utilizing said scores SD( ) to partition the training images in into H different semantic clusters of training images. 6. The process of claim 5 , wherein the pair-wise annotation-based semantic distance score SD( ) for a particular pair of training images in is given by the equation SD ⁡ ( a , b ) = 1 2 ⁢ n 1 ⁢ ∑ i = 1 n 1 ⁢ min j ⁢ SD ⁡ ( a i , b j ) + 1 2 ⁢ n 2 ⁢ ∑ j = 1 n 2 ⁢ min i ⁢ SD ⁡ ( a i , b j ) , wherein, a is the vector of keyword annotations for one image in the pair, n 1 is a total number of keywords in a, and a i is a particular keyword in a, and b is the vector of keyword annotations for the other image in the pair, n 2 is a total number of keywords in b, and b j is a particular keyword in b.