Abstract
The paper presents a new approach of finding nearest neighbor in image classification algorithm by proposing efficient method for similarity measure. Generally in supervised classification, after finding the feature vectors of training images and testing images, nearest neighbor classifier does the classification job. This classifier uses different distance measures such as Euclidean distance, Manhattan distance etc. to find the nearest training feature vector. This paper proposes to use Mean Squared Error (MSE) to find the nearness between two images. Initially Independent Principal Component Analysis (PCA),which we discussed in our earlier work, is applied to images of each class to generate Eigen coordinate system for that class. Then for the given test image, a set of feature vectors is generated. New images are reconstructed using each Eigen coordinate system and the corresponding test feature vector. Lowest MSE between the given test image and new reconstructed image indicates the corresponding class for that image. The experiments are conducted on COIL-100 database. The performance is also compared with distance based nearest neighbor classifier. Results show that the proposed method achieves high accuracy even for small size of training set.
Highlights
With the explosion of image data on the Internet and the availability of large-scale image databases, automatically classifying these large collections of images is becoming an important challenge
Reliable
Feature extraction follows by various classification methods like nearest neighbor classifier[13], artificial neural network[14], support vector machine[15], genetic algorithm[16] and so on
Summary
With the explosion of image data on the Internet and the availability of large-scale image databases , automatically classifying these large collections of images is becoming an important challenge. Feature extraction follows by various classification methods like nearest neighbor classifier[13], artificial neural network[14], support vector machine[15], genetic algorithm[16] and so on. Some distance measure such as Euclidean distance is used for comparing feature vectors. Given a set of multivariate measurements, the purpose of PCA is to find a smaller set of variables with less redundancy, that would give as good a representation as possible It reduces the dimensionality of the description by projecting the points onto the principal axes, where orthonormal set of points are in the direction of maximum covariance of the data. The eigenvector with the highest Eigen value is the principle component of the data set
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