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

在研究项目中，我们开发了利用图像局部特征对历史文献数据库进行快速检索的方法。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。1基于PCA特征空间方法（PCA）的历史文献检索应用于文献图像匹配，实现了对文献数据库的快速检索。该方法将字符串图像分割成狭缝状的小图像，并利用特征空间方法进行降维。2.梯度直方图检索将PCA向量细化为梯度直方图的特征向量。3局部特征匹配的应用我们利用方法1开发了一个基于文档中词频模式的文本映射软件。4利用并行处理器快速计算局部特征SURF图像特征算法在MX-1并行处理器上实现。该处理器加快了尺度检测和梯度分布计算的速度。5局部特征向量的局部敏感哈希算法讨论了用于历史文档再现的快速字典检索方法。实现了局部敏感哈希算法，用于文档图像检索，从字符图像样本字典中查找相似字符图像。

In the research projects we developed fast retrieval method of historical document database using local feature of images.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 The followings are the outcomes of the project.1 Historical document retrieval based on PCAEigenspace method (PCA) applied to document image matching showed fast retrieval of document database.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of PCA to 95-98 percent.3 Application of local feature matchingWe developed a transcript mapping software for historical document based on the pattern of frequency of words in the document using method 1.4 Fast computation of local feature using parallel processorAlgorithms of SURF image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Locality sensitive hashing of local feature vectorWe discussed fast dictionary retrieval method for reproduction of historical document.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