Semantic Classification of Images in Hierarchical Manner using Fuzzy Rules and HSVM Classifier

Abstract

Hierarchical structure classification is more effective in concept based classification. The feature vector is varying with the different concepts, though it belongs to the same class. Images which are similar in appearance are not correlated semantically. Hence an author made an attempt to classify the images in the dataset in concept wise in a hierarchical manner. For this, an author manually clustered the same concept images in a layer by layer to explore the semantic correlation of the concepts. A number of nodes in the hierarchy tree represent the number of classes in the dataset. Each node is classified into sub-nodes by the fuzzy classifier using fuzzy association rules. Fuzzy inference system trained by the fuzzy rules base, which is generated by the fuzzy feature vector. The performance of the fuzzy classifier is validated with multiclass support vector machine classifier and hierarchical support vector machine classifier. The dataset used here is COREL dataset; an author selected 8 concepts for the classification.

Keywords: Fuzzy classifier, fuzzy rule, hierarchical SVM, multiclass SVM, semantic concepts

Cite this Article: Thirumala Lakshmi K, Usha Kingsly Devi K. Semantic Classification of Images in Hierarchical Manner using Fuzzy Rules and HSVM Classifier. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(2): 33–54p.