Abstract:
This study proposes a new feature descriptor, local directional mask maximum edge pattern, for image retrieval
and face recognition applications. Local binary pattern (LBP) and LBP variants collect the relationship between the centre
pixel and its surrounding neighbours in an image. Thus, LBP based features are very sensitive to the noise variations in an
image. Whereas the proposed method collects the maximum edge patterns (MEP) and maximum edge position patterns
(MEPP) from the magnitude directional edges of face/image. These directional edges are computed with the aid of
directional masks. Once the directi
onal edges (DE) are computed, the
MEP and MEPP are coded based on the
magnitude of DE and position of maximum DE. Further, the robustness of the proposed method is increased by
integrating it with the multiresolution Gaussian filters. The performance of the proposed method is tested by
conducting four experiments onopen access series of imagin
g studies-magnetic resonance imaging, Brodatz, MIT
VisTex and Extended Yale B databases for biomedical image retrieval, texture retrieval and face recognition
applications. The results after being investigated the proposed method shows a significant improvement as compared
with LBP and LBP variant features in terms of their evaluation measures on respective databases.
