  In this paper, a new texture descriptor based on the local neighborhood
intensity difference is proposed for content based image retrieval (CBIR). For
computation of texture features like Local Binary Pattern (LBP), the center
pixel in a 3*3 window of an image is compared with all the remaining neighbors,
one pixel at a time to generate a binary bit pattern. It ignores the effect of
the adjacent neighbors of a particular pixel for its binary encoding and also
for texture description. The proposed method is based on the concept that
neighbors of a particular pixel hold a significant amount of texture
information that can be considered for efficient texture representation for
CBIR. Taking this into account, we develop a new texture descriptor, named as
Local Neighborhood Intensity Pattern (LNIP) which considers the relative
intensity difference between a particular pixel and the center pixel by
considering its adjacent neighbors and generate a sign and a magnitude pattern.
Since sign and magnitude patterns hold complementary information to each other,
these two patterns are concatenated into a single feature descriptor to
generate a more concrete and useful feature descriptor. The proposed descriptor
has been tested for image retrieval on four databases, including three texture
image databases - Brodatz texture image database, MIT VisTex database and
Salzburg texture database and one face database AT&T face database. The
precision and recall values observed on these databases are compared with some
state-of-art local patterns. The proposed method showed a significant
improvement over many other existing methods.
