Open AccessExtended Adjacent Local Binary Pattern for Texture Based Image Retrieval
Author(s) -
S. Ramakrishnan,
S. Nithya,
L. Meenachi,
Alireza Janani
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1767/1/012043
Subject(s) - pixel , binary number , diagonal , artificial intelligence , image (mathematics) , binary image , local binary patterns , mathematics , texture (cosmology) , pattern recognition (psychology) , computer vision , computer science , image processing , histogram , geometry , arithmetic
In this paper, a novel local texture descriptor named Extended Adjacent Local Binary Pattern (EALBP) for images is proposed. In this method, each image is subdivided into local regions of size 4*4. For each local region, 13-bits of binary value are generated by comparing the adjacent neighborhood pixel in three phases. In the first phase, the inner 2x2 region from the local region is considered and each pixel is compared with the adjacent pixel which results in 4 bits. In the second phase, the outer region from the local region is considered and the diagonal pixels present in the 4 directions such as 45°, 135°, 225° and 315° are considered. The pixels values present in each direction are compared with the adjacent neighborhood pixels in the outer region and this comparison results in 4 bits. In the third phase, the pixels except the diagonal pixel values in the outer region are compared against each other and this result in 4 bit binary value. Finally, the mean of the local region is compared with the mean of the input image which results in one bit binary value. The 13-bit binary value of each local region is thus formed and the above steps are repeated for the entire image. The experimental results show that the proposed method could characterize the texture property of the image efficiently when compared with the existing methods on two databases.