期刊名称：International Journal of Multimedia and Ubiquitous Engineering
印刷版ISSN：1975-0080
出版年度：2015
卷号：10
期号：11
页码：303-314
DOI：10.14257/ijmue.2015.10.11.29
出版社：SERSC
摘要：Automatic image annotation is one of crucial and attractive field of image retrieval. Classification process is part of the important phase in automatic image annotation (AIA). With the explosive growth of methods in this research area, this paper proposes 5 processing steps before image annotation using Amazon dataset, i.e., image segmentation, object identification, feature extraction, feature selection and image features classification. A lot of research has been done in creating numbers of different approaches and algorithm for image segmentation. Otsu is one of the most well known method in image segmentation region based. The proposed model aims to provide the highest accuracy after undergo those processing steps. This paper conducted several experiments for image classification starting from image segmentation in order to demonstrate usefulness and competiveness among different type of classifiers. It also target to study the effect of morphological operation and feature selection to the accuracy. For the classification experiment, it was tested using four types of classifiers: BayesNet, NaiveBayesUpdateable, RandomTree and IBk.
