Combined Center-Symmetric Local Patterns for Image Recognition

Parsi, Bhargav; Tyagi, Kunal; Malwe, Shweta R.

doi:10.1007/978-981-10-7512-4_30

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…A total of 17 local pattern descriptors that are divided into four categories: Four local ternary patterns (LTP), namely: SILTP [ 18 ], CSLTP [ 19 ], CSSILTP, and XCSLTP [ 20 ]; Two local derivative patterns (LDP), namely: Center-Symmetric LDP (CSLDP) [ 21 ] and Center-Symmetric Local Deritative Mapped Pattern (CSLDMP). Two local mapped patterns (LMP), namely: eXtended Center-Symmetric LMP (XCSLMP) [ 22 ] and Center-Symmetric LMP (CSLMP) [ 23 ]. Nine local binary patterns LBP), namely: eXtended Center-Symmetrical LBP (XCSLBP) [ 10 ], Center-Symmetric LBP (CSLBP) [ 24 ], Elliptical-LBP (ELBP) [ 25 ], LBP-NRI Uniform [ 26 ], LBP-ROR [ 27 ], LBP-Uniform [ 28 ], OLBP [ 29 ], SCSLBP [ 30 ], and VARLBP [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Hand-Crafted and Learned Feature Aggregation for Visual Marble Tiles Screening

et al. 2022

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An important factor in the successful marketing of natural ornamental rocks is providing sets of tiles with matching textures. The market price of the tiles is based on the aesthetics of the different quality classes and can change according to the varying needs of the market. The classification of the marble tiles is mainly performed manually by experienced workers. This can lead to misclassifications due to the subjectiveness of such a procedure, causing subsequent problems with the marketing of the product. In this paper, 24 hand-crafted texture descriptors and 20 Convolution Neural Networks were evaluated towards creating aggregated descriptors resulting from the combination of one hand-crafted and one Convolutional Neural Network at a time. A marble tile dataset designed for this study was used for the evaluation process, which was also released publicly to further enable the research for similar studies (both on texture and dolomitic ornamental marble tile analysis). This was done to automate the classification of the marble tiles. The best performing feature descriptors were aggregated together in order to achieve an objective classification. The resulting model was embodied into an automatic screening machine designed and constructed as a part of this study. The experiments showed that the aggregation of the VGG16 and SILTP provided the best results, with an AUC score of 0.9944.

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