Content-Based Building Image Retrieval

• Main Authors: Huang, Chi-Ming, 黃啟銘
• Other Authors: Chen, Zen
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/90211998041399296755

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 98 === 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.