000845718 000__ 06995cam\a2200649Mi\4500 000845718 001__ 845718 000845718 005__ 20230306145033.0 000845718 006__ m\\\\\o\\d\\\\\\\\ 000845718 007__ cr\cn\nnnunnun 000845718 008__ 180801s2018\\\\sz\a\\\\ob\\\\101\0\eng\d 000845718 019__ $$a1048596201$$a1050602153 000845718 020__ $$a9783319977850$$q(electronic book) 000845718 020__ $$a3319977857$$q(electronic book) 000845718 020__ $$z9783319977843 000845718 020__ $$z3319977849 000845718 0247_ $$a10.1007/978-3-319-97785-0$$2doi 000845718 035__ $$aSP(OCoLC)on1049848966 000845718 035__ $$aSP(OCoLC)1049848966$$z(OCoLC)1048596201$$z(OCoLC)1050602153 000845718 040__ $$aUCW$$beng$$erda$$cUCW$$dOCLCO$$dGW5XE$$dYDX$$dUAB 000845718 049__ $$aISEA 000845718 050_4 $$aQ327$$b.S67 2018 000845718 08204 $$a006.3$$223 000845718 1112_ $$aS+SSPR (Workshop)$$d(2018 :$$cBeijing, China) 000845718 24510 $$aStructural, Syntactic, and Statistical Pattern Recognition :$$bJoint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17-19, 2018, Proceedings /$$cXiao Bai, Edwin R. Hancock, Tin Kam Ho, Richard C. Wilson, Battista Biggio, Antonio Robles-Kelly (eds.). 000845718 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2018] 000845718 300__ $$a1 online resource (xiii, 524 pages) :$$billustrations 000845718 336__ $$atext$$btxt$$2rdacontent 000845718 337__ $$acomputer$$bc$$2rdamedia 000845718 338__ $$aonline resource$$bcr$$2rdacarrier 000845718 347__ $$atext file$$bPDF$$2rda 000845718 4901_ $$aLecture Notes in Computer Science,$$x0302-9743 ;$$v11004 000845718 4901_ $$aLNCS Sublibrary: SL6 : Image Processing, Computer Vision, Pattern Recognition, and Graphics 000845718 504__ $$aIncludes bibliographical references and index. 000845718 5050_ $$aClassification and Clustering -- Image annotation using a semantic hierarchy -- Malignant Brain Tumor Classification using the Random Forest Method -- Rotationally Invariant Bark Recognition -- Dynamic voting in multi-view learning for radiomics applications -- Iterative Deep Subspace Clustering -- A scalable spectral clustering algorithm based on landmark-embedding and cosine similarity -- Deep Learning and Neural Networks -- On Fast Sample Preselection for Speeding up Convolutional Neural Network Training -- UAV First View Landmark Localization via Deep Reinforcement Learning -- Context Free Band Reduction Using a Convolutional Neural Network -- Local Patterns and Supergraph for Chemical Graph Classification with Convolutional Networks -- Learning Deep Embeddings via Margin-based Discriminate Loss -- Dissimilarity Representations and Gaussian Processes -- Protein Remote Homology Detection using Dissimilarity-based Multiple Instance Learning -- Local Binary Patterns based on Subspace Representation of Image Patch for Face Recognition -- An image-based representation for graph classification -- Visual Tracking via Patch-based Absorbing Markov Chain -- Gradient Descent for Gaussian Processes Variance Reduction -- Semi and Fully Supervised Learning Methods -- Sparsification of Indefinite Learning Models -- Semi-supervised Clustering Framework Based on Active Learning for Real Data -- Supervised Classification Using Feature Space Partitioning -- Deep Homography Estimation with Pairwise Invertibility Constraint -- Spatio-temporal Pattern Recognition and Shape Analysis -- Graph Time Series Analysis using Transfer Entropy -- Analyzing Time Series from Chinese Financial Market Using A Linear-Time Graph Kernel -- A Preliminary Survey of Analyzing Dynamic Time-varying Financial Networks Using Graph Kernels -- Few-Example Affine Invariant Ear Detection in the Wild -- Line Voronoi Diagram using Elliptical Distances -- Structural Matching -- Modelling the Generalised Median Correspondence through an Edit Distance -- Learning the Graph Edit Distance edit costs based on an embedded model -- Ring Based Approximation of Graph Edit Distance -- Graph Edit Distance in the exact context -- The VF3-Light Subgraph Isomorphism Algorithm: when doing less is more effective -- A Deep Neural Network Architecture to Estimate Node Assignment Costs for the Graph Edit Distance -- Error-Tolerant Geometric Graph Similarity -- Learning Cost Functions for Graph Matching -- Multimedia Analysis and Understanding -- Matrix Regression-based Classification for Face Recognition -- Plenoptic Imaging for Seeing Through Turbulence -- Weighted Local Mutual Information for 2D-3D Registration in Vascular Interventions -- Cross-model Retrieval with Reconstruct Hashing -- Deep Supervised Hashing with Information Loss -- Single Image Super Resolution via Neighbor Reconstruction -- An Efficient Method for Boundary Detection from Hyperspectral Imagery -- Graph-Theoretic Methods -- Bags of Graphs for Human Action Recognition -- Categorization of RNA Molecules using Graph Methods -- Quantum Edge Entropy for Alzheimer's Disease Analysis -- Approximating GED using a Stochastic Generator and Multistart IPFP -- Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks -- On Association Graph Techniques for Hypergraph Matching -- Directed Network Analysis using Transfer Entropy Component Analysis -- A Mixed Entropy Local-Global Reproducing Kernel for Attributed Graphs -- Dirichlet Densifiers: Beyond Constraining the Spectral Gap. 000845718 506__ $$aAccess limited to authorized users. 000845718 520__ $$aThis book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2018, held in Beijing, China, in August 2018. The 49 papers presented in this volume were carefully reviewed and selected from 75 submissions. They were organized in topical sections named: classification and clustering; deep learning and neurla networks; dissimilarity representations and Gaussian processes; semi and fully supervised learning methods; spatio-temporal pattern recognition and shape analysis; structural matching; multimedia analysis and understanding; and graph-theoretic methods. . 000845718 588__ $$aDescription based on online resource; title from digital title page (viewed on September 20, 2018). 000845718 650_0 $$aPattern perception$$vCongresses. 000845718 650_0 $$aComputer science$$vCongresses. 000845718 650_0 $$aData structures (Computer science)$$vCongresses. 000845718 650_0 $$aAlgorithms$$vCongresses. 000845718 650_0 $$aComputer science$$xMathematics$$vCongresses. 000845718 650_0 $$aArtificial intelligence$$vCongresses. 000845718 650_0 $$aImage processing$$vCongresses. 000845718 7001_ $$aXiao, Bai,$$eeditor. 000845718 7001_ $$aHancock, Edwin R.$$eeditor. 000845718 7001_ $$aHo, Tin Kam.$$eeditor. 000845718 7001_ $$aWilson, Richard C.$$eeditor. 000845718 7001_ $$aBiggio, Battista.$$eeditor. 000845718 7001_ $$aRobles-Kelly, Antonio.$$eeditor. 000845718 77608 $$iPrint version: $$z9783319977843 000845718 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics. 000845718 830_0 $$aLecture notes in computer science ;$$v11004.$$x0302-9743 000845718 852__ $$bebk 000845718 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-97785-0$$zOnline Access$$91397441.1 000845718 909CO $$ooai:library.usi.edu:845718$$pGLOBAL_SET 000845718 980__ $$aEBOOK 000845718 980__ $$aBIB 000845718 982__ $$aEbook 000845718 983__ $$aOnline 000845718 994__ $$a92$$bISE