Leseprobe
Contents
1 Introduction
1.1 Motivation
1.2 Problem Statement
1.3 Objectives
1.4 Structure of this Thesis
2 A brief history and overview of Approximate Nearest-Neighbor Queries
2.1 Improving Index Structures
2.2 Softening Requirements for Accuracy
2.3 Further Related Work
3 An Introduction to SRS-12
3.1 Why SRS-12 is worth a consideration
3.2 SRS-12 in a Nutshell
3.3 Variants of SRS-12
3.4 Indexing
3.5 Normal Stopping Condition of the SRS-12 Algorithm
4 Implementation & Experimental Evaluation
4.1 Implementation Details for the Computation of Parameter Values .
4.2 Datasets
4.3 Veri cation on SIFT1M Dataset
4.4 Block Matching
4.4.1 A Brief Introduction to Block Matching
4.4.2 Methodology
4.4.3 Parameter Settings
4.4.4 Computation Time
4.4.5 Conclusions
4.4.6 Summary - Recommended Parameter Settings
4.5 Computation Time of SRS-12 vs Exact Nearest-Neighbor Search
4.5.1 Possible Optimizations
5 Future Work
5.1 Image Prediction
5.2 Reducing Data through Rapid Object Detection and Background Removal . . .
5.3 Optical Flow
5.4 Increasing the Accuracy of the SRS-12 Algorithm
6 Discussion
Bibliography
- Arbeit zitieren
- Philipp Güth (Autor:in), 2015, Predicting image data with the SRS-12 algorithm. Applying random projection-based c-approximate nearest-neighbor queries to image data, München, GRIN Verlag, https://www.grin.com/document/305214
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