近年來，隨著網際網路的盛行、各種資料儲存容量的增大及數位影像的普及，數位影像資料量日益龐大。因此，以內容為基礎之圖片擷取與分類逐漸成為近年來許多研究的主題。過去已有許多研究在探討圖片分類的方法，但這些方法大多著重於利用擷取圖片中低階的特徵（如顏色、形狀與紋理等），應用分類上的方法去尋找圖片分類法則。然而，利用這些低階的特徵並無法完整的表現出整張圖片的意義，以人的觀點而言，利用圖片物件間的關聯關係的分類法才是較佳的方式。在本篇論文中，我們提出一個利用圖片物件間的階層式關聯關係來建立圖片分類法則的方法。該方法分為兩個部分：1)建立物件階層架構。2)探勘分類法則。在第一部份我們利用圖片物件的低階特徵，配合資料探勘的階層式分群方法，建立物件的階層架構。第二部分則是利用圖片物件與其階層架構，配合多階層式關聯法則探勘法，尋找物件的階層式關聯規則，並進一步建立圖片分類法則。而藉由實驗的觀察，我們所提出之方法與其他方法比較，皆有較佳的分類預測準確度。

　　With the growth of the World Wide Web, the improved storage techniques and popularity of digital images have led to the proliferation of images. Contented-based image retrieval and classification have become important research issues in the last few years. There exist a number of researches concerning image classifications, but most of them are focused on using low-level image features (e.g. color, texture, shape, etc.) and do not consider the conceptual associations between the objects in the images. In this paper, we propose a new image classification method by using multi-level association rule based on image objects. The method is composed of two parts: 1) Building of conceptual object hierarchy, 2) Discovery of classification rules. In the first part, we use a hierarchical clustering method to build the o conceptual object hierarchy based on the low-level features of image objects. At the second part, we devise a multi-dimensional multi-level association rule mining algorithm for finding the image classification rules. Through experimental evaluations, our method is shown to have higher accuracy in classifying images than other tested methods.

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