This paper presents an efficient method for texture retrieval using multiscale feature extraction and embedding based on the local extrema keypoints. The idea is to first represent each texture image by its local maximum and local minimum pixels. The image is then divided into regular overlapping blocks and each one is characterized by a feature vector constructed from the radiometric, geometric and structural information of its local extrema. All feature vectors are finally embedded into a covariance matrix which will be exploited for dissimilarity measurement within retrieval task. Thanks to the method's simplicity, multiscale scheme can be easily implemented to improve its scale-space representation capacity. We argue that our handcrafted features are easy to implement, fast to run but can provide very competitive performance compared to handcrafted and CNN-based learned descriptors from the literature. In particular, the proposed framework provides highly competitive retrieval rate for several texture databases including 94.95% for MIT Vistex, 79.87% for Stex, 76.15% for Outex TC-00013 and 89.74% for USPtex.
Multiscale Analysis for Improving Texture Classification
École de Technologie Supérieure, University of Québec（魁北克大学高等技术学院）；Instituto Federal do Sul de Minas Gerais（南米纳斯吉拉斯联邦学院）；Pontificia Universidade Catolica do Paraná（巴拉那天主教大学）
Region Feature Descriptor Adapted to High Affine Transformations
School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机科学学院）；Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education（教育部智慧医疗技术工程研究中心）
Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane
Centro de Investigaciones Ópticas (CONICET La Plata - CIC)（光学研究中心（CONICET La Plata - CIC））；Universidad Nacional de La Plata (UNLP)（拉普拉塔国立大学）；Universidade Estadual de Maringá（马林加州立大学）
From Species to Cultivar: Soybean Cultivar Recognition using Multiscale Sliding Chord Matching of Leaf Images
School of Engineering, Griffith University（格里菲斯大学工程学院）；School of Computer Science and Technology, Wuhan University of Technology（武汉理工大学计算机科学与技术学院）；Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences（中国科学院东北地理与农业生态研究所）
Spatio-spectral networks for color-texture analysis
São Carlos Institute of Physics, University of São Paulo（圣保罗大学圣卡洛斯物理研究所）；Institute of Mathematics and Computer Science, University of São Paulo（圣保罗大学数学与计算机科学研究所）
Feature Engineering is Not Dead: Reviving Classical Machine Learning with Entropy, HOG, and LBP Feature Fusion for Image Classification
Department of Physics and Engineering Physics, Tulane University（物理与工程物理系，路易斯安那大学）；Institute of Applied Geosciences, Karlsruhe Institute of Technology (KIT)（应用地球科学研究所，卡尔斯鲁厄技术大学）；Cure and Care Wellness Pvt Ltd（治愈与关怀健康私人有限公司）；Amritha Vidya Vishwapeetham（阿米特哈大学）
