Mohammed Ahmed Talab,Siti Norul Huda Sheikh Abdullah,Mohammad Hakim Assiddiq Razalan
Abstract
Invariant descriptor for shape and texture image
recognition usage is an essential branch of pattern recognition.
It is made up of techniques that aim at extracting information
from shape images via human knowledge and works. The
descriptors need to have strong Local Binary Pattern (LBP) in
order to encode the information distinguishing them. Local
Binary Pattern (LBP) ensures encoding global and local
information and scaling invariance by introducing a look-up
table to reflect the uniformity structure of an object. It is
needed as the edge direction matrices (EDMS) only apply
global invariant descriptor which employs first and secondary
order relationships. The main objective of this paper is the
need of improved recognition capabilities which achieved by
the combining LBP and EDMS. Working together, these two
descriptors will add advantages to the program and enable the
researcher to investigate the weaknesses of each one. Two
classifiers are used: multi-layer neural network and random
forest. The techniques used in this paper are compared with
Gray-Level Co-occurrence matrices (GLCM-EDMS) and Scale
Invariant Feature Transform (SIFT) by using two benchmark
dataset: MPEG-7 CE-Shape-1 for shape and Arabic
calligraphy for texture. The experiments have shown the
superiority of the introduced descriptor over the GLCMEDMS
and the SIFT.
محمد الزيدي | Mohammed Ahmed Talab Al-zaidi | 6428