Improved Classification of Content-Based Image Features Using Hybrid Classification Decision

Feature vector extraction is a significant aspect of content-based image classification. Researchers have proposed multiple techniques for representing the image content in the form of feature vectors using important image properties like shape, colour, texture, etc. However, a single feature vector extracted using a particular technique is mostly unable to capture important details of images. This work has attempted a decision fusion-based classification approach using two different features extracted with image binarization and image transform technique, respectively. The results of decision fusion for classification have outperformed the individual approaches.