A Study on Image Retrieval Technique Based on the Features of Leaves

• Main Authors: Tsui-Yun Chang, 張翠雲
• Other Authors: Kuo-Feng Hwang
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/07742321134707095615

碩士 === 國立臺中技術學院 === 多媒體設計研究所 === 94 === The development of computer technology has made possible many things that seemed almost inconceivable only a few decades ago. By employing computer systems, complex calculations can be processed within a short period of time, such as the retrieval of a digital image from a large database that matches the input image. In this manner, the use of image retrieval systems in identifying different species of plants has become a worthwhile research topic. The leaf shape can provide important information in the identification of plants. The patterns of a leaf can be described by several shape factors, such as compactness and elongation. However, many descriptors are incapable of providing sensitive information about the leaf shape, leading to less efficient system performance while searching an image database. Our research uses dimensionless shape factors and general shape factors to develop a leaf image retrieval system. In addition, several novel shape descriptors are proposed for the identification of idealized leaf types. 　　Using a database of live leaf images, the first scheme developed a leaf image retrieval system based on three shape factors: compactness, elongation and symmetry. Among these factors, compactness and elongation are dimensionless shape descriptors used frequently in many related researches. However, the measurement of elongation is limited in usefulness, since elongation cannot effectively express the distribution of image pixels. Therefore, this study incorporates the concept of horizontal projection to provide more information on pixel distribution in an image. The horizontal symmetric parts of a leaf can be detected using the symmetry descriptor. Experimental results based on 670 leaf images from 67 plants show that the proposed approach can achieve an efficient retrieval performance. 　　The second scheme utilizes general descriptors to identify 28 idealized leaf and petal types. Because dimensionless shape factors cannot be used to recognize leaf types with certain orientations, thus they are only capable of identifying approximately half (57%) of the 28 idealized leaf types. According to the results of this analysis, we found that there are a number of issues that cannot be resolved with traditional dimensionless shape factors, including: (i) differences between two leaves that have the same shape, but with inversed directions, and (ii) similar leave shapes with minor variations. To solve these issues, we presented two new shape descriptors, T-axis ratio and Product of Area and Height (PAH). Results from the experiment incorporating these shape descriptors show a 21% improvement over the traditional method.