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

在研究项目中，我们提出了一种利用图像局部特征的历史文档库快速检索方法。我们还开发了用于MX-1并行处理器的局部特征检测代码和用于快速字符识别的LSH算法。将基于PCAE特征空间方法的历史文档检索(PCA)应用于文档图像匹配，实现了文档数据库的快速检索。对字符串图像进行切分，利用特征空间方法对图像进行降维。检索性能为73%-93%。2.基于梯度直方图的检索我们将基于主成分分析的特征向量细化为梯度直方图。3局部特征匹配的应用利用方法1.4利用并行处理器快速计算局部特征在MX-1并行处理机上实现了SURF图像特征的快速计算。该处理器加速了尺度检测的计算和梯度分布的计算。5.局部特征向量的局部敏感散列。讨论了用于历史文档再现的快速词典检索方法。我们实现了文档图像检索的局部敏感哈希算法，从字符图像实例词典中搜索相似的字符图像。

In the research projects we developed fast retrieval method of historical document database using local feature of images.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 The followings are the outcomes of the project.1 Historical document retrieval based on PCAEigenspace method (PCA) applied to document image matching showed fast retrieval of document database.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of PCA to 95-98 percent.3 Application of local feature matchingWe developed a transcript mapping software for historical document based on the pattern of frequency of words in the document using method 1.4 Fast computation of local feature using parallel processorAlgorithms of SURF image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Locality sensitive hashing of local feature vectorWe discussed fast dictionary retrieval method for reproduction of historical document.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.