Patent ID: 8873844

Claim:
A method for metric learning, comprising: iteratively determining feature groups of images based on its derivative norm; learning corresponding metrics of the feature groups by gradient descent based on an expected loss; combining the corresponding metrics to provide an intermediate metric matrix as a sparse representation of the images; determining κ = argmax κ ∈ 1 , 2 , … , K ⁢  ∏ PSD ⁢ ⁢ ( - ⌊ ∂ f ⁡ ( A | χ ) ∂ A ⌋ κκ )  2 ⁢ A κ ⁢ * , ⁢ α * = argmin A κ ≥ 0 , A κ ∈ B κκ , α ∈ ℛ + ⁢ ⁢ f ⁡ ( α ⁢ ⁢ A + A κ | χ ) ⁢ A → α * ⁢ A + A κ * where K feature groups comprise x=[x (1) ,x (2) , . . . ,x (K) ] T ε D ,x (K) ε D and where x (K) is the k-th feature group with d features and the concatenated feature dimensionality D=Kd with Mahanalobis matrix A, training set χ, weak metrics A κ * corresponding to effective feature groups; optimizing, using a processor, a loss function of all metric parameters corresponding to features of the intermediate metric matrix to learn a final metric matrix; and projecting eigenvalues of the final metric matrix onto a simplex.