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.
Highlights
Classification is a supervised method that categorizes the unknown data into a specific class or group
Image classification is used for indexing, categorization and annotation of the images
The LMKNN classifier determines the neighbor of an input sample based on the difference between the mean values of the nearest training samples and an input sample
Summary
Classification is a supervised method that categorizes the unknown data into a specific class or group. The k nearest neighbor (kNN) is a memory-based and non -parametric classification algorithm where the algorithm memorizes the training samples to predict the class label of an unknown sample. The LMKNN classifier determines the neighbor of an input sample based on the difference between the mean values of the nearest training samples and an input sample It has good classification accuracy but it has some limitations: the same value of k is used for different classes and all neighbors have uniform weights. In [11] the PNN method was proposed for classification based on the similarity between the weighted distance of an input sample and pseudo training samples. To improve the accuracy of kNN based classification, the harmonic mean based weighted nearest neighbor classifier is proposed which is insensitive to the value of k.
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