A system level design for object location and identification in unstructured environments

Thesis (Mech. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1998.Includes bibliographical references (leaves 81-84).by Rolland L. Doubleday, Jr.Mech.E

Digital photo album management techniques: from one dimension to multi-dimension.

Lu Yang.Thesis submitted in: November 2004.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 96-103).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Our Contributions --- p.3Chapter 1.3 --- Thesis Outline --- p.5Chapter 2 --- Background Study --- p.7Chapter 2.1 --- MPEG-7 Introduction --- p.8Chapter 2.2 --- Image Analysis in CBIR Systems --- p.11Chapter 2.2.1 --- Color Information --- p.13Chapter 2.2.2 --- Color Layout --- p.19Chapter 2.2.3 --- Texture Information --- p.20Chapter 2.2.4 --- Shape Information --- p.24Chapter 2.2.5 --- CBIR Systems --- p.26Chapter 2.3 --- Image Processing in JPEG Frequency Domain --- p.30Chapter 2.4 --- Photo Album Clustering --- p.33Chapter 3 --- Feature Extraction and Similarity Analysis --- p.38Chapter 3.1 --- Feature Set in Frequency Domain --- p.38Chapter 3.1.1 --- JPEG Frequency Data --- p.39Chapter 3.1.2 --- Our Feature Set --- p.42Chapter 3.2 --- Digital Photo Similarity Analysis --- p.43Chapter 3.2.1 --- Energy Histogram --- p.43Chapter 3.2.2 --- Photo Distance --- p.45Chapter 4 --- 1-Dimensional Photo Album Management Techniques --- p.49Chapter 4.1 --- Photo Album Sorting --- p.50Chapter 4.2 --- Photo Album Clustering --- p.52Chapter 4.3 --- Photo Album Compression --- p.56Chapter 4.3.1 --- Variable IBP frames --- p.56Chapter 4.3.2 --- Adaptive Search Window --- p.57Chapter 4.3.3 --- Compression Flow --- p.59Chapter 4.4 --- Experiments and Performance Evaluations --- p.60Chapter 5 --- High Dimensional Photo Clustering --- p.67Chapter 5.1 --- Traditional Clustering Techniques --- p.67Chapter 5.1.1 --- Hierarchical Clustering --- p.68Chapter 5.1.2 --- Traditional K-means --- p.71Chapter 5.2 --- Multidimensional Scaling --- p.74Chapter 5.2.1 --- Introduction --- p.75Chapter 5.2.2 --- Classical Scaling --- p.77Chapter 5.3 --- Our Interactive MDS-based Clustering --- p.80Chapter 5.3.1 --- Principal Coordinates from MDS --- p.81Chapter 5.3.2 --- Clustering Scheme --- p.82Chapter 5.3.3 --- Layout Scheme --- p.84Chapter 5.4 --- Experiments and Results --- p.87Chapter 6 --- Conclusions --- p.94Bibliography --- p.9

Redundancy on content-based indexing.

by Cheung King Lum Kingly.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 108-110).Abstract --- p.iiAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Problems in Content-Based Indexing --- p.2Chapter 1.3 --- Contributions --- p.3Chapter 1.4 --- Thesis Organization --- p.4Chapter 2 --- Content-Based Indexing Structures --- p.5Chapter 2.1 --- R-Tree --- p.6Chapter 2.2 --- R+-Tree --- p.8Chapter 2.3 --- R*-Tree --- p.11Chapter 3 --- Searching in Both R-Tree and R*-Tree --- p.15Chapter 3.1 --- Exact Search --- p.15Chapter 3.2 --- Nearest Neighbor Search --- p.19Chapter 3.2.1 --- Definition of Searching Metrics --- p.19Chapter 3.2.2 --- Pruning Heuristics --- p.21Chapter 3.2.3 --- Nearest Neighbor Search Algorithm --- p.24Chapter 3.2.4 --- Generalization to N-Nearest Neighbor Search --- p.25Chapter 4 --- An Improved Nearest Neighbor Search Algorithm for R-Tree --- p.29Chapter 4.1 --- Introduction --- p.29Chapter 4.2 --- New Pruning Heuristics --- p.31Chapter 4.3 --- An Improved Nearest Neighbor Search Algorithm --- p.34Chapter 4.4 --- Replacing Heuristics --- p.36Chapter 4.5 --- N-Nearest Neighbor Search --- p.41Chapter 4.6 --- Performance Evaluation --- p.45Chapter 5 --- Overlapping Nodes in R-Tree and R*-Tree --- p.53Chapter 5.1 --- Overlapping Nodes --- p.54Chapter 5.2 --- Problem Induced By Overlapping Nodes --- p.57Chapter 5.2.1 --- Backtracking --- p.57Chapter 5.2.2 --- Inefficient Exact Search --- p.57Chapter 5.2.3 --- Inefficient Nearest Neighbor Search --- p.60Chapter 6 --- Redundancy On R-Tree --- p.64Chapter 6.1 --- Motivation --- p.64Chapter 6.2 --- Adding Redundancy on Index Tree --- p.65Chapter 6.3 --- R-Tree with Redundancy --- p.66Chapter 6.3.1 --- Previous Models of R-Tree with Redundancy --- p.66Chapter 6.3.2 --- Redundant R-Tree --- p.70Chapter 6.3.3 --- Level List --- p.71Chapter 6.3.4 --- Inserting Redundancy to R-Tree --- p.72Chapter 6.3.5 --- Properties of Redundant R-Tree --- p.77Chapter 7 --- Searching in Redundant R-Tree --- p.82Chapter 7.1 --- Exact Search --- p.82Chapter 7.2 --- Nearest Neighbor Search --- p.86Chapter 7.3 --- Avoidance of Multiple Accesses --- p.89Chapter 8 --- Experiment --- p.90Chapter 8.1 --- Experimental Setup --- p.90Chapter 8.2 --- Exact Search --- p.91Chapter 8.2.1 --- Clustered Data --- p.91Chapter 8.2.2 --- Real Data --- p.93Chapter 8.3 --- Nearest Neighbor Search --- p.95Chapter 8.3.1 --- Clustered Data --- p.95Chapter 8.3.2 --- Uniform Data --- p.98Chapter 8.3.3 --- Real Data --- p.100Chapter 8.4 --- Discussion --- p.102Chapter 9 --- Conclusions and Future Research --- p.105Chapter 9.1 --- Conclusions --- p.105Chapter 9.2 --- Future Research --- p.106Bibliography --- p.10