以內容為基礎的建築物影像檢索

Content-Based Building Image Retrieval

黃啟銘(Chi-Ming Huang)

指導教授 ： 陳稔

國立交通大學/資訊學院/資訊科學與工程研究所/碩士(2010年)

https://doi.org/10.6842/NCTU.2010.00575

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。

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

並列摘要

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.

並列關鍵字

building image retrieval ； MSER ； Zernike Moment ； kd-tree ； ZuBud

參考文獻

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