Fast Retrieval of Massive linage Archives based on Similarity of I cleat Feature Descriptor

基于I cleat特征描述符相似度的海量谱系档案快速检索

基本信息

批准号：
18300061
负责人：
KAWASHIMA Toshio
金额：
$ 4.35万
依托单位：
Future University-Hakodate
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18300061/
关键词：
ARCHIVES IMAGE RETRIEVAL LOCAL DESCRIPTOR HASHING

项目摘要

In the research projects we developed fast retrieval method of historical document database using local feature of images. We also developed codes for local feature detection for MX-1 parallel processors, and LSH algorithm for fast character recognition. The followings are the outcomes of the project.1 Historical document retrieval based on PCAEigenspace method (PCA) applied to document image matching showed fast retrieval of document database. Image of character strings are sliced into slit-wise small images and reduced the dimension using eigenspace method. The performance of retrieval for was 73-93 percent.2 Retrieval using gradient histogramWe refined feature vector based on PCA vector to gradient histogram. The approach improved the performance of PCA to 95-98 percent.3 Application of local feature matchingWe developed a transcript mapping software for historical document based on the pattern of frequency of words in the document using method 1.4 Fast computation of local feature using parallel processorAlgorithms of SURF image feature are implemented on a MX-1 parallel processor. Computation of scale detection and gradient distribution calculation are accelerated using the processor.5 Locality sensitive hashing of local feature vectorWe discussed fast dictionary retrieval method for reproduction of historical document. We implemented Locality Sensitive Hashing algorithm for document image retrieval to search similar character image from dictionary of character image examples.

在研究项目中，我们使用图像的局部功能开发了历史文档数据库的快速检索方法。我们还开发了用于MX-1并行处理器的局部特征检测的代码，以及用于快速字符识别的LSH算法。以下是项目的结果。1基于应用于文档图像匹配的PCAEIGENSPACE方法（PCA）的历史文档检索显示了文档数据库的快速检索。字符串的图像将其切成狭缝的小图像，并使用特征空间方法降低了尺寸。检索的性能为73-93％。2使用基于PCA矢量到梯度直方图的梯度直方图We的梯度直方图矢量。该方法改善了PCA的性能至95-98％。3局部功能匹配的应用基于文档中单词频率的模式，使用方法1.4使用Surf图像特征的并行处理器对文档中的单词频率开发了一个历史文档的软件，该软件在MX-1并行处理器上实现。使用处理器加速规模检测和梯度分布计算的计算。5局部特征vectorwe的局部敏感性哈希讨论了快速词典检索方法，用于复制历史文档。我们为文档图像检索实现了局部性敏感的哈希算法，以搜索字符图像示例字典中的相似字符图像。