PCA based feature extraction for cell image retrieval - Ananthagunan Muthukumaran (2009)
In this Dissertation work a Software programme has been developed which implement Principal Component Analysis for Cell Image retrieval:Principal component analysis, based on information theory concepts, seek a computational model that best describes a cell image, by extracting the most relevant information contained In that image. The Eigenimage approach is a principal component analysis method, in which a small set of characteristic pictures are used to describe the variation between cell images. The goal is to find out the eigenvectors (eigenimage) of the covariance matrix of the distribution, spanned by a training sot of cell mages. Later, every cell image is represented by a linear combination of these eigenvectors. Evaluations of these eigenvectors are quite difficult for typical image sizes but an approximation that is suitable for practical purposes is also presented. Recognition is performed by projecting a new image into the subspace spanned by the Eigen images and then classifying the image by comparing its position in Eigen space with the positions of known individuals. The Euclidian Distance method is adapted to find out the position of the new image in the subspace After Calculating the Euclidian Distance the images, which are stored, are retrieved back in the order of closeness with that of the test image. This is done by using the feature vector and Eigen images which are derived using PCA.