  In image based feature descriptor design, local information from image
patches are extracted using iterative scanning operations which cause high
computational costs. In order to avoid such scanning operations, we present
matrix multiplication based local feature descriptors, namely a Matrix
projection based Local Binary Pattern (M-LBP) descriptor and a Matrix
projection based Histogram of Oriented Gradients (M-HOG) descriptor.
Additionally, an integrated formulation of M-LBP and M-HOG (M-LBP-HOG) is also
proposed to perform the two descriptors together in a single step. The proposed
descriptors are evaluated using a publicly available mammogram database. The
results show promising performances in terms of classification accuracy and
computational efficiency.
