Open AccessAlgorithms for Extracting various Local Texture Features
Author(s) -
B. Ashwath Rao,
N Gopalakrishna Kini
Publication year - 2022
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 - 10.1088/1742-6596/2161/1/012067
Subject(s) - computer science , texture (cosmology) , artificial intelligence , search engine indexing , image texture , texture filtering , correctness , image (mathematics) , pattern recognition (psychology) , feature (linguistics) , texture compression , feature extraction , computer vision , texture synthesis , algorithm , image processing , linguistics , philosophy
In the machine learning and computer vision domain, images are represented using their features. Color, shape, and texture are some of the prominent types of features. Over time, the local features of an image have gained importance over the global features due to their high discerning ability in localized regions. The texture features are widely used in image indexing and content-based image retrieval. In the last two decades, various local texture features have been formulated. For a complete description of images, effective and efficient features are necessary. In this paper, we provide algorithms for 10 local texture feature extraction. These texture descriptors have been formulated since the year 2015. We have designed algorithms so that they are time efficient and memory space-efficient. We have implemented these algorithms and verified their output correctness.