Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A retrieval technique for similar shapes
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
Efficient retrieval for browsing large image databases
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Dimensionality reduction for similarity searching in dynamic databases
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the effects of dimensionality reduction on high dimensional similarity search
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Searching Multimedia Databases by Content
Searching Multimedia Databases by Content
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Similarity Searching in Medical Image Databases
IEEE Transactions on Knowledge and Data Engineering
Fast and Effective Retrieval of Medical Tumor Shapes
IEEE Transactions on Knowledge and Data Engineering
3D Shape Histograms for Similarity Search and Classification in Spatial Databases
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On Feature Selection through Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Hi-index | 0.00 |
We propose a statistical approach based on a supervised framework for reducing the dimensionality of the feature space when characterizing and classifying spatial Regions of Interest (ROIs). Our approach employs the statistical techniques of Bootstrapping simulation, Bayesian Inference and Markov Chain Monte Carlo (MCMC), to select the most informative features according to their discriminative power across distinct classes of data. This reduces the dimensionality of the initial feature space and also improves the classification of the ROIs, since features providing irrelevant information with respect to class membership are discarded. We also introduce a weighted Euclidean Distance designed to effectively classify the ROIs. We evaluate the proposed technique using experiments that involve synthetic spatial regions and real ROIs extracted from medical images. We demonstrate its effectiveness in classification experiments (using established classifiers) and in similarity searches. We also test its scalability on large datasets. Our approach is comparable with or better than other major competitors. We achieve an accuracy of 87% on classifying ROIs in brain images. These results are an improvement of previously reported classification experiments, and show the effect of reducing the dimensionality of the initial feature space.