Shape-Based Single Object Classification Using Ensemble Method Classifiers
This work addresses image annotation and retrieval challenges for databases like Amazon and Google, but it is incremental as it applies existing ensemble methods to known datasets.
The paper tackled the problem of multi-category image classification for single object images by proposing a hierarchical classification framework to bridge the semantic gap, achieving classification accuracies ranging from 20% to 99% across different classifiers, with Bagging performing best.
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.