Shape-Based Single Object Classification Using Ensemble Method Classifiers

TL;DR

This paper presents a hierarchical classification framework for single object image classification that combines preprocessing, object identification, and ensemble classifiers, achieving up to 99% accuracy on Amazon and Google datasets.

Contribution

It introduces a novel hierarchical framework integrating ensemble classifiers for effective single object image classification.

Findings

Bagging classifier achieved the highest accuracy.

Classification accuracies ranged from 20% to 99%.

Ensemble methods outperform individual classifiers.

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.