Geometrically invariant color, shape and texture features for object recognition using multiple kernel learning classification approach

DOI10.1016/j.ins.2019.01.058zbMath1448.68444OpenAlexW2913377457WikidataQ128535837 ScholiaQ128535837MaRDI QIDQ2213101

Chandan Singh, Jaspreet Singh

Publication date: 27 November 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2019.01.058

zbMATH Keywords

multiple kernel learningrotation invariantmulti-channel Zernike momentstexture descriptor

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Machine vision and scene understanding (68T45)

Uses Software

• LIBSVM
• SIMPLIcity
• ImageNet
• SURF
• AlexNet

Cites Work

• Quaternion Bessel-Fourier moments and their invariant descriptors for object reconstruction and recognition
• Image analysis by Bessel-Fourier moments
• Quaternion polar harmonic Fourier moments for color images
• Error analysis in the computation of orthogonal rotation invariant moments
• Color image analysis by quaternion-type moments
• Accurate calculation of Zernike moments
• A Unified Methodology for Computing Accurate Quaternion Color Moments and Moment Invariants
• Quaternionic Local Ranking Binary Pattern: A Local Descriptor of Color Images
• Multichannel Decoded Local Binary Patterns for Content-Based Image Retrieval
• A Zernike Moment Phase-Based Descriptor for Local Image Representation and Matching
• Local Color Vector Binary Patterns From Multichannel Face Images for Face Recognition

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