A hierarchical multi-class classification system for face and text datasets

In an era of rapidly growing multimedia data, the need for robust and efficient classification systems has become critical, specifically the identification of class names and poses or styles. This study provides an understanding of the organization of data, and feature selection (i.e., edge) using the k-means segmentation technique is explained. Furthermore, for the optimization of features, the linear regression technique is used. The optimized features can be directly used with classifiers, but to reduce the noise, outliers are identified and removed from the training data. The classifiers are involved in training and recognizing the face or text class label. After the prediction of class labels, the distance matrix-based technique is used to identify the style or pose name. Finally, the experiments are conducted with the help of the ORL dataset (40 classes and 10 poses in each class) and character dataset (36 characters and 10 font styles in each character). The experimental results indicated that the proposed methodology accurately classifies hierarchically organized data and demonstrates superiority over KNN and Bayesian-based classification when compared to support vector machine (SVM). The system provides classification outcomes with up to 100% accuracy for outlier-removed data, and up to 98% for basic features. Unlike traditional flat classification approaches, our system leverages hierarchical structures to enhance classification accuracy, scalability, and interpretability.