Harmonic Mean based Classification of Images using Weighted Nearest Neighbor for Tagging

Abstract

On image sharing websites, the images are associated with the tags. These tags play a very important role in an image retrieval system. So, it is necessary to recommend accurate tags for the images. Also, it is very important to design and develop an effective classifier that classifies images into various sematic categories which is the necessary step towards tag recommendation for the images. The performance of existing tag recommendation based on k nearest neighbor methods can be affected due to the number of k neighbors, distance measures, majority voting irrespective of the class and outlier present in the k-neighbors. To increase the accuracy of the classification and to overcome the issues in existing k nearest neighbor methods, the Harmonic Mean based Weighted Nearest Neighbor (HM-WNN) classifier is proposed for the classification of images. Given an input image, the HM-WNN determines k nearest neighbors from each category for color and texture features separately over the entire training set. The weights are assigned to the closest neighbor from each category so that reliable neighbors contribute more to the accuracy of classification. Finally, the categorical harmonic means of k nearest neighbors are determined and classify an input image into the category with a minimum mean. The experimentation is done on a self-generated dataset. The result shows that the HM-WNN gives 88.01% accuracy in comparison with existing k-nearest neighbor methods.