Feature extraction for object recognition using PCA-KNN with application to medical image analysis

This paper provides a new feature extraction method for object recognition using PCA-KNN algorithm with SIFT descriptor. The proposed method is divided into three steps. The first step is based on feature extraction from the input images using SIFT (Scale Invariant Feature Transform) descriptor. Each of the features is represented using one or more feature descriptors. In medical systems images used as patterns are also represented by feature vectors. In the second step eigen values and eigen vectors are extracted from each image. We apply PCA algorithm after we reduce the number of features by SIFT algorithm. The goal is to extract the important information as a set of new orthogonal variables called principal components. In the final step a nearest neighbor classifier is designed for classifying the images based on the extracted features. The algorithm is experimented in MATLAB and tested with the Caltech 101 database and the experimental results are shown.