Image Classification and Recognition Using PCA
Abstract— This paper demonstrates the classification of images using principal component analysis. PCA is an image classification algorithm which presents accuracy of classification. First, the image should be pre-processed that is the images are in different sizes, some may be noisy or larger in size, so all the images are resized to a specific size, then it is easy to classify the images. Second, the images are colorful, so it is converted into grey scale image color images are 3-dimensional it is hard to process an 3d image so it is converted into grey scale which is 2d in nature. PCA is an image classification technique which extracts the major features of an image. First, the system is trained using some images, in order to produce an image feature. After it is trained, when a new image is passed it compares with the image feature and displays the output image based on the input. The development of image classification has been improved due to growth in volume of images, as well as the widespread application in multiple fields. Final output is to retrieve the related images based on the input.
Index Terms— Classification, Eigen value, Eigen Vector, Image, Principal Component.
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International Journal for Trends in Technology & Engineering © 2015 IJTET JOURNAL