🤖 AI Summary
Accurate shape-aware classification of single-object images—particularly in e-commerce settings—remains challenging due to the semantic gap between low-level geometric representations and high-level semantics.
Method: We propose a hierarchical shape-feature classification framework that bridges this gap by integrating image segmentation with object recognition pre- and post-processing. Leveraging shape features, we construct four distinct classifiers based on Bayesian networks, random forests, Bagging, and voting ensembles, and conduct the first systematic evaluation of ensemble strategies for single-object classification. Experiments employ 10-fold cross-validation on Amazon and Google single-object datasets.
Results/Contributions: (1) Bagging achieves 99% classification accuracy—significantly outperforming baselines—validating the effectiveness of synergistic shape-feature and Bagging modeling; (2) ensemble learning is empirically established as superior for fine-grained single-object classification; (3) the framework supports scalable automated annotation and cross-platform image retrieval.
📝 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.