2019
DOI: 10.1007/s11042-019-7207-2
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Texture image Classification based on improved local Quinary patterns

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Cited by 55 publications
(45 citation statements)
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“…The main goal of this paper is to propose an efficient and accurate approach for texture classification. Thus, to get results and observe the efficiency of proposed approach five class labels of Brodatz [21][22] dataset were selected to collect dataset. Next, each class image is divided to 16 non-overlap images.…”
Section: Resultsmentioning
confidence: 99%
“…The main goal of this paper is to propose an efficient and accurate approach for texture classification. Thus, to get results and observe the efficiency of proposed approach five class labels of Brodatz [21][22] dataset were selected to collect dataset. Next, each class image is divided to 16 non-overlap images.…”
Section: Resultsmentioning
confidence: 99%
“…Many descriptors are designed for the extraction of texture such as LBP is designed for the description of local contrast and spatial structure of an image , LQP is introduced in advancement of LBP to improve the accuracy of classification but have some disadvantages like its input parameters are static and don't providing important binary patterns. [35] Designed an improved version of LQP, known as improved LQP that divided the code of local quinary to four patterns based on local pattern, consists of local features. Evaluation of proposed approach based on data sets of Brodatz and Outex.…”
Section: Related Workmentioning
confidence: 99%
“…Armi et al [35] proposed the improved local quinary patterns (ILQP) to overcome some disadvantages of LQP and thus improve its performance. The definition of the local quinary pattern in ILQP is the same as in the LQP operator.…”
Section: Improved Local Quinary Patterns (Ilqp)mentioning
confidence: 99%