Thai heritage images classification by Naïve Bayes image classifier

Digital Library is a way to represent, retrieval, and study Thai E-san culture heritages without directly accessing and touching. It may help to reduce a dilapidation of Thai E-san culture heritages. We commence our project with organizing a collection of images through classification technique. Therefore, this work is motivated by two main drivers. Firstly, we aim to apply an alternative dimension of CBIR to classify a collection of Thai E-san heritage images into two classes: the class of heritage images which involve human activities, and the class of heritage images with non-human activities (e.g. images of ancient remains and antiques). Secondly, we also propose a new method of images classification. It is to apply Naïve Bayes to produce image classifier based on edge histogram features. This approach is valuable for the automatically classifying heritage images, where it is a time-consuming and labour-intensive process if it is done by manual classification. After testing, the experimental results show an effective accuracy. This would demonstrate that our approach is sufficiently reliable for use.