CASIA OpenIR  > 毕业生  > 硕士学位论文
自动商标检索系统
其他题名Automatic Trademark Retrieval System
宛根训
学位类型工学硕士
导师刘迎建
2003-05-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词基于内容图像检索 特征抽取 层次检索 相关反馈 Content-based Image Retrieval Feature Extraction Hierarchical Retrieval Relevance Feedback
摘要随着注册商标数量的急剧增加,在这种超大图像库上实现检索、比对将需要耗费大量的时间和人力。本文主要针对这种大图像数据库进行研究,开发出高效、准确的自动检索系统,减少传统的关键词检索方法存在的缺陷,提高商 标注册的准确性并缩短注册的时间。 检索的高效性和准确性是基于内容图像检索的两个关键。多年来,许多研 究人员在这个领域提出了许多有效的方法,但是还远没有达到完善。本文在讨 论一般图像检索理论的同时,结合商标图像的特点,建议了一套新的检索方法, 在检索的高效性和准确性两个方面都取得了较好的效果。主要包含下面几个工 作: 1.在预处理环节,考虑到商标图像作为人工图像和自然图像存在的差别,从 实际出发,先对商标图像进行二值化,并在抽取特征之前进行去噪、去文 本、归一化和边缘检测等一系列预处理,消除位移、放缩等影响,增加特 征抽取的鲁棒性。 2.利用多种特征作为识别的基础。本文除了利用不变矩、边缘方向、小波能 量等常用的全局特征,为了更好地描述商标图像,还引入字符识别领域常 用的局部形状特征来强调商标图像的局部信息。最后利用概率主成分分析 (PPCA)来降低特征的维数,加快匹配的速度。 3.层次检索策略和相关反馈。利用层次检索策略,在基本不影响精度的前提 下可以大大提高检索的速度;相关反馈引入人的主观判断能力,通过学习 人的主观感受,提高查询的效果。 为了验证算法的有效性,我们进行了一组实验,取得了良好的效果。本文 针对商标图像来进行的,但是很多设计思想和算法可以用于许多其他大图像数 据库的检索问题。在商标图像库上开发二套符合人的感知的自动检索系统是一 件非常困难和有挑战性的问题,但是自动检索方法具有非常强大的功能,可以 解决传统的商标比对方法所需要耗费大量的时间和人力,带来很大的方便。
其他摘要With the increase of register trademark, there will consume many time and effort for human to retrieve and compare a trademark with large trademark database. In this paper, we dedicate to studying and developing an efficiency and precision system in this large trademark image database, which reduce the limitation of-traditional keyword annotation retrieval method and improve trademark register quality and speed. Retrieval efficiency and accuracy are two important issues in content-based image retrieval field. Content-based image retrieval (CBIR) has been extensively studied for many years and a number of techniques have been proposed. However, it is still a difficult task and the result is still far from practical. In this thesis, on the basis of generic CBIR theory and considering trademark image features, we propose a new retrieval system. This system achieves both desired efficiency and accuracy. The main contents of the thesis are as follows. 1. In preprocess stage, considering the difference between trademark image and general natural image, the trademark images are converted to binary images firstly. And then, removing noise, eliminating words and texts, normalization and edge detection are processed orderly which will eliminate the effect of transition and scale and increase the robust of feature extraction. 2. Multi-feature extraction method is adopted to improve the accuracy of system. We not only use some global feature such as invariant moment, edge direction, wavelet energy etc, but also introduce excellent local shape feature in character recognition field such as direction element feature (DEF), which emphasize local information of trademark. Lastly, we use probability principal component analysis (PPCA) to reduce dimension of original feature, which accelerate feature match. 3. Hierarchical retrieval and relevance feedback. Using hierarchical retrieval method, the accuracy and speed of retrieval are both ensured. Relevance feedback introduces subjective perception of user and improves the accuracy of retrieval The experiment results verify the valid of system. Though the system is designed for trademark image, most of the ideas and algorithms can be applied to other large image database retrieval problems. We believe that it is a difficult and challenge to develop an automatic retrieval method which models the human perception well. However, considering the time and effort for humans to perform such a retrieval task, an automatic retrieval method can provide a powerful tool to facilitate the retrieval task for human beings.
馆藏号XWLW681
其他标识符681
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6838
专题毕业生_硕士学位论文
推荐引用方式
GB/T 7714
宛根训. 自动商标检索系统[D]. 中国科学院自动化研究所. 中国科学院研究生院,2003.
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