# Shape-Based Single Object Classification Using Ensemble Method Classifiers Source: [Shape-Based Single Object Classification Using Ensemble Method Classifiers]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:45:57.319Z Authors: Nur Shazwani Kamarudin, Mokhairi Makhtar, Syadiah Nor Wan Shamsuddin, Syed Abdullah Fadzli Published: 2025-01-16T05:58:32.000Z ## 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 well known 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()