Image Compression Methods using Dimension Reduction and Classification through PCA and LDA: A Review
Abstract: This paper presents in depth survey on various techniques of compression methods. Linear Discriminant analysis (LDA) is a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be for dimensionality reduction before later classification. Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of variables of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The purpose of the review is to explore the possibility of a combined approach for image compression in which the best features of LDA and PCA shall be used. Another purpose of the study is to explore the possibility of image compression for multiple images.
Keywords: Image Compression, Dimension Reduction, Linear Discriminant Analysis LDA, Principal Component Analysis PCA
How to Cite?: Khushboo Kumar Sahu, Prof. K. J. Satao, "Image Compression Methods using Dimension Reduction and Classification through PCA and LDA: A Review", Volume 5 Issue 5, May 2016, International Journal of Science and Research (IJSR), Pages: 2277-2280, https://www.ijsr.net/getabstract.php?paperid=NOV163957, DOI: https://dx.doi.org/10.21275/NOV163957