Similarity-Based Pattern Recognition
First International Workshop, SIMBAD 2011, Venice, Italy, September 28-30, 2011, Proceedings
Published on 25. September 2011
XIII, 333 pages
978-3-642-24471-1 (ISBN)
Description
This book constitutes the proceedings of the First International Workshop on Similarity Based Pattern Recognition, SIMBAD 2011, held in Venice, Italy, in September 2011.
The 16 full papers and 7 poster papers presented were carefully reviewed and selected from 35 submissions. The contributions are organized in topical sections on dissimilarity characterization and analysis; generative models of similarity data; graph-based and relational models; clustering and dissimilarity data; applications; spectral methods and embedding.
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Marcello Pelillo | Edwin R. Hancock
Similarity-Based Pattern Recognition
First International Workshop, SIMBAD 2011, Venice, Italy, September 28-30, 2011, Proceedings
Book
09/2011
1st Edition
Springer
€53.49
Shipment within 7-9 days
Content
- Title page
- Preface
- Organization
- Table of Contents
- On the Usefulness of Similarity Based Projection Spaces for Transfer Learning
- Introduction
- Notations
- Learning with Good Similarity Functions
- Domain Adaptation
- Modifying the Projection Space for Domain Adaptation
- A Normalization of a Similarity Function
- An Additional Regularization Term for Moving Closer the Two Distributions
- Experiments
- Synthetic Toy Problem
- Image Classification
- Conclusion
- Metric Anomaly Detection via Asymmetric Risk Minimization
- Introduction
- Theoretical Results
- Preliminaries
- Known Separation Distance
- Definition of Risk
- Classification Rule
- No Explicit Prior on
- Experiments
- Methodology
- Data Sets
- Results
- Discussion and Future Work
- References
- One Shot Similarity Metric Learning for Action Recognition
- Introduction
- Related Work
- One-Shot-Similarity Metric Learning (OSSML)
- The Free-Scale LDA-Based, Symmetric OSS Score
- Deriving the OSSML
- Objective Function
- Free-Scale LDA-Based OSS Gradient
- Application to Action Recognition
- ASLAN Data Set
- Same/Not-Same Benchmark
- Experimental Setup
- Experimental Results
- Conclusion
- References
- On a Non-monotonicity Effect of Similarity Measures
- Introduction
- Construction Principles of Similarity Measures Induced by the Aggregation of Element-Wise Operating Functions
- f-Divergence Measures
- The Monotonicity Property of the Discrepancy Measure
- Impact of the Non-monotonicity Effect on Applications
- Image Tracking
- Stereo Matching
- Defect Detection in Textured Surfaces
- Conclusion and Future Work
- References
- Section-Wise Similarities for Clustering and Outlier Detection of Subjective Sequential Data
- Introduction
- CABINTEC Database
- Data Acquisition
- Trend Segmentation Algorithm
- Similarity Definitions
- Mean Level Based Similarity
- Angle Based Similarity
- Experiments
- Clustering
- Outlier Detection
- Conclusions
- References
- Hybrid Generative-Discriminative Nucleus Classification of Renal Cell Carcinoma
- Introduction
- Background: The Probabilistic Latent Semantic Analysis
- The Tissue Microarray (TMA) Pipeline
- Tissue Micro Arrays
- Image Normalization and Patching
- Segmentation
- Feature Extraction
- Nuclei Classification
- Experiments
- Discussion
- Conclusion
- References
- Multi-task Regularization of Generative Similarity Models
- Introduction
- Background on Local Similarity Discriminant Analysis
- Multi-task Regularization of Mean Similarity Estimates
- Closed-Form Solution
- Choice of Task Relatedness A
- Related Work in Multi-Task Learning
- Benchmark Classification Results
- Iraqi Insurgent Rhetoric Analysis
- Discussion and Open Questions
- References
- A Generative Dyadic Aspect Model for Evidence Accumulation Clustering
- Introduction
- Generative Model for Evidence Accumulation Clustering
- Clustering Ensembles and Evidence Accumulation
- Generative Model
- The Expectation Maximization Algorithm
- The E-Step
- The M-Step
- Summary of the Algorithm and Interpretation of the Estimates
- Related Work
- Experimental Results and Discussion
- Conclusions and Future Work
- References
- Supervised Learning of Graph Structure
- Introduction
- Generative Graph Model
- Correspondence Sampler
- Estimating the Model
- Model Selection
- Experimental Evaluation
- Shock Graphs
- 3D Shapes
- Synthetic Data
- Edge-Weighted Graphs
- Conclusions
- References
- An Information Theoretic Approach to Learning Generative Graph Prototypes
- Introduction
- Probabilistic Framework
- Model Coding Using MDL
- Encoding Sample Graphs
- Encoding the Supergraph Model
- Expectation-Maximization
- Weighted Code-Length Function
- Maximization
- Expectation
- Experiments
- Conclusion
- References
- Graph Characterization via Backtrackless Paths
- Introduction
- Backtrackless Walks on Graphs
- Kernels for Labeled Graphs
- Pattern Vectors for Unlabeled Graphs
- Experiments
- Synthetic Data
- Real-World Dataset
- Timing Analysis
- Strengths and Weaknesses
- Conclusion
- References
- Impact of the Initialization in Tree-Based Fast Similarity Search Techniques
- Introduction
- The MDF-Tree
- Building an MDT-Tree
- The Search Algorithm
- Initialization Methods
- Random Method
- Outlier Method
- Median Method
- Experiments
- Non Balanced Trees
- Conclusions
- References
- Multiple-Instance Learning with Instance Selection via Dominant Sets
- Introduction
- Background
- Instance-Selection Based MIL
- Clustering with Dominant Sets
- Proposed Method
- Notations
- Instance Selection with Dominant Sets
- Classification
- Extension to Multi-class MIL
- Computational Complexity
- Experimental Results
- Benchmark Data Sets
- Image Classification
- Sensitivity to Labeling Noise
- Summary and Future Work
- References
- Min-sum Clustering of Protein Sequences with Limited Distance Information
- Introduction
- Preliminaries
- Algorithm Overview
- Algorithm Analysis
- Algorithm Description
- Structure of the Clustering Instance
- Proof of Theorem 1 and Additional Analysis
- Experimental Results
- Conclusion
- References
- Model-Based Clustering of Inhomogeneous Paired Comparison Data
- Introduction
- Relevant Work
- Modeling Paired Comparison Data
- Model-Based Clustering
- Missings
- Model Inference
- Selection of Comparisons
- Application: Preference Prediction
- Experimental Results
- Synthetic Data
- Political Goals German Data
- Conclusion
- References
- Bag Dissimilarities for Multiple Instance Learning
- Introduction
- Bag Dissimilarities
- Bag Distribution Dissimilarities
- Pairwise Instance Dissimilarities
- Linear Assignment Dissimilarity
- Standard MIL Classifiers
- Experiments
- Conclusions
- References
- Mutual Information Criteria for Feature Selection
- Introduction
- Dominant-Set Clustering Algorithm
- Concept of Dominant Set
- Dominant-Set Clustering Algorithm
- Feature Similarity Measure
- Feature Selection Using Dominant-Set Clustering
- Computing the Similarity Matrix
- Dominant-Set Clustering
- Selecting Key Features
- Classification
- Experiments and Comparisons
- Cluster Performance Evaluation Using Different Similarity Measures
- Classification Results Using Selected Feature Subset
- Conclusions
- References
- Combining Data Sources Nonlinearly for Cell Nucleus Classification of Renal Cell Carcinoma
- Introduction
- Data Set
- Tissue Micro Arrays
- Image Normalization and Patching
- Segmentation
- Feature Extraction
- Methodology
- Linear Multiple Kernel Learning
- Nonlinear Multiple Kernel Learning
- Experiments
- Experimental Methodology
- Results
- Discussion
- Conclusion
- References
- Supervised Segmentation of Fiber Tracts
- Introduction
- Methods
- Basic Definitions and Notation
- Evaluation Criteria
- Distances
- Classification Algorithms and Feature Space
- Dissimilarity Space
- Fiber Tract Segmentation
- Experiments and Results
- Dataset: PBCC2009 Spring Edition
- Single Subject Segmentation
- Predictions Cross-Subjects
- Discussion
- References
- Exploiting Dissimilarity Representations for Person Re-identification
- Introduction
- Background
- Previous Works on Person Re-identification
- The Multiple Component Matching Framework
- The Multiple Component Dissimilarity Framework for Person Re-identification
- Application of MCD
- Conclusions and Future Work
- References
- A Study of Embedding Methods under the Evidence Accumulation Framework
- Introduction
- Embedding Methods
- Nonlinear Methods
- Linear Methods
- Evidence Accumulation: The Co-association Matrix
- Dimensionality Reduction in Evidence Accumulation Clustering
- Quality Measures
- Experimental Results
- Data
- Experiment 1: Feature Space
- Experiment 2: Similarity Space
- Discussion
- Conclusions
- References
- A Study on the Influence of Shape in Classifying Small Spectral Data Sets
- Introduction
- Introduction to Dissimilarity Representation Approach
- 1D and 2D Dissimilarity Measures for Spectral Data
- Experimental Section
- Data sets
- Experiments and Discussion
- Discussion and Conclusions
- References
- Feature Point Matching Using a Hermitian Property Matrix
- Introduction
- Complex Laplacian (Hermitian) Matrix
- Expectation Maximization
- E-Step
- M-Step
- Experimental Results
- Conclusions
- References
- Author Index
