@inproceedings{a8724ed270024aaa82a4e981b08bc6cb,
title = "Image classification using GMM with context information and with a solution of singular covariance problem",
abstract = "Summary form only given. Taking the average of feature vectors from the center and neighboring blocks to a block being coded is proposed as a method of considering context information in block classification. The algorithm has the advantage of low complexity. Gauss mixture models (GMM) are adopted to extract features from image blocks, including an algorithm to handle singular covariance matrices. Two different distortion measures are used; namely log-likelihood quadratic discrimination analysis (QDA) and a dimension-compensated distortion measure defined by dividing the QDA distortion by the corresponding cell's dimension. Aerial images were used to train and test. Experimental results show that the proposed algorithm not only improves the classification performance, but also provides a solution to the singular covariance problem.",
keywords = "Covariance matrix, Data mining, Discrete cosine transforms, Distortion measurement, Feature extraction, Frequency, Gaussian processes, Image classification, Sun, Testing",
author = "Sangho Yoon and Won, \{Chee Sun\} and Kyungsuk Pyun and Gray, \{Robert M.\}",
note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; Data Compression Conference, DCC 2003 ; Conference date: 25-03-2003 Through 27-03-2003",
year = "2003",
doi = "10.1109/DCC.2003.1194076",
language = "English",
series = "Data Compression Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "457",
editor = "Storer, \{James A.\} and Martin Cohn",
booktitle = "Proceedings - DCC 2003",
address = "United States",
}