標題: | 以內容為基礎的建築物影像檢索 Content-Based Building Image Retrieval |
作者: | 黃啟銘 Huang, Chi-Ming 陳稔 Chen, Zen 資訊科學與工程研究所 |
關鍵字: | 建築物影像檢索;影像特徵擷取;影像特徵描述;交通大學建築物;影像辨識;building image retrieval;MSER;Zernike Moment;kd-tree;ZuBud |
公開日期: | 2009 |
摘要: | 本論文目的在使用影像區域特徵來建立一個建築物影像檢索系統。此檢索系統分成資料庫與查詢兩個部份,資料庫部份按照處理順序又可分為三個步驟,第一步驟使用可抗視角變化的Maximally Stable Extremal Region做特徵區域擷取;第二步驟使用旋轉不變的phased-based Zernike Moment做特徵區域描述;第三步驟使用kd-tree建立特徵向量的索引。建立資料庫時,使用同一棟建築物相鄰的影像特徵互相比對,去除不穩定出現的特徵區域,並使用Density-Based Spatial Clustering of Applications with Noise分群法,以減少資料庫中存在的儲存重覆特徵問題。查詢部分採用kd-tree找最近點與鄰近點的便利性,以直觀的投票機制找出資料庫中與查詢影像最相似的建築物。 The goal of this thesis research is to construct a building image indexing and retrieval system. This system consists of two parts: the database organization (indexing) and the query part (retrieval). The database part is further composed of three modules. In the first module, view-invariant feature detection, Maximally Stable Extremal Region (MSER), is used to extract the regions of interest. In the second module, the phased-based Zernike Moment is used to describe the regions. In the third module, a kd-tree structure is used to establish the index of Zernike Moment feature vectors. When constructing the database, in order to eliminate the unstable regions, a trick of comparison of the features extracted from the neighboring views of the same building is used. To reduce the problem of redundancy, the clustering algorithm, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), is used. In the query part, the kd-tree provides a convenient way to find the nearest neighbor. And then an intuitive voting mechanism is used to find the building from the database which is most similar to the query image. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT079755608 http://hdl.handle.net/11536/45955 |
Appears in Collections: | Thesis |
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