用集成方法提升单物体图像分类准确率，最高达99%。
Shape-Based Single Object Classification Using Ensemble Method Classifiers
- 构建分层框架，结合预处理与后处理技术
- Bagging分类器准确率达99%，优于随机森林等其他方法
- 适合图像检索与标注场景，尤其对小样本数据有效
随着图像数据激增，图像标注与检索面临分类挑战，每类由具有相同语义标签的数据库图像组成。本文提出一种分层分类框架，有效弥合语义鸿沟，实现多类别图像分类。采用经典的预处理与后处理方法，应用于图像分割、目标识别和图像分类三个任务。在Amazon和Google数据集上测试了四种分类器：贝叶斯网络（BN）、随机森林（RF）、Bagging和投票法。10折交叉验证结果显示，分类准确率介于20%至99%之间。其中Bagging表现最佳，其次为随机森林。
原文摘要 · Abstract (English)
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.
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