Shape-Based Single Object Classification Using Ensemble Method Classifiers

Abstract

Abstract-Nowadays, more and more images are available. Annotation and retrieval of the images pose classification problems, where each class is defined as the group of database images labelled with a common semantic label. Various systems have been proposed for content-based retrieval, as well as for image classification and indexing. In this paper, a hierarchical classification framework has been proposed for bridging the semantic gap effectively and achieving multi-category image classification. A wellknown pre-processing and post-processing method was used and applied to three problems; image segmentation, object identification and image classification. The method was applied to classify single object images from Amazon and Google datasets. The classification was tested for four different classifiers; BayesNetwork (BN), Random Forest (RF), Bagging and Vote. The estimated classification accuracies ranged from 20% to 99% (using 10-fold cross validation). The Bagging classifier presents the best performance, followed by the Random Forest classifier.

The application of UAV images in flood detection using image segmentation techniques

“…Kuala Nerus is a complex residential area with high number of population since it is situated nearby to the two public universities in Terengganu which are Universiti Malaysia Terengganu (UMT) and Universiti Sultan Zainal Abidin (UniSZA) [20]. The focused study area of this research is Taman Desa Tanjung Damai, a complex residential area in Kuala Nerus, Terengganu, Malaysia.…”