JPEG compressed image retrieval via statistical features

To improve efficiency of compressed image retrieval, we propose a novel statistical feature extraction algorithm in this paper to characterize the image content directly in its compressed domain. The statistical feature extracted is mainly through computing a set of moments directly from DCT coefficients without involving full decompression or inverse DCT. Following the algorithm design, a content-based image retrieval system is implemented especially targeting retrieving joint picture expert group compressed images. Theoretical analysis and experimental results support that the system is robust to translation, rotation and scale transform with minor disturbance, and the system achieves good performances in terms of retrieval efficiency and effectiveness.     

基于纹理谱描述子的文档图像检索算法

新算法首先根据文档图像的特点分割图像文本区域,并将文档图像中字符的边缘信息使用纹理谱进行描述,计算纹理谱图像的直方图。相对于直接使用灰度直方图进行图像检索,该算法具有更好的区分度。实验结果表明,该方法具有很高的查准率,并对剪切、旋转操作表现出很好的稳定性,适合文档图像检索。     

基于DCT压缩的JPEG图像的快速检索

提出了一种基于离散余弦变换(DCT)压缩的JPEC图像的检索方法。该方法利用JPEG图像数据在DCT压缩域的特性,直接提取特征,而且只需要对JPEC进行部分熵解码。在加速了图像检索的过程的同时也保证了检索结果的精确性,并且具有一定的鲁棒性。     

基于流行排序的多示例图像检索方法

为了提高图像检索的性能,提出了一种基于流行排序的多示例图像检索方法,将分割后的图像表示为多示例的形式,通过给出适合图像在包空间的度量方式,有效结合流行排序和多示例学习的方法来进行图像检索.实验结果表明,采用所提出的方法的检索结果与传统的检索方法相比,检索率得到了明显的提高,检索结果更符合人的视觉习惯.     

A multi-level matching method with hybrid similarity for document retrieval

This paper presents a multi-level matching method for document retrieval (DR) using a hybrid document similarity. Documents are represented by multi-level structure including document level and paragraph level. This multi-level-structured representation is designed to model underlying semantics in a more flexible and accurate way that the conventional flat term histograms find it hard to cope with. The matching between documents is then transformed into an optimization problem with Earth Mover’s Distance (EMD). A hybrid similarity is used to synthesize the global and local semantics in documents to improve the retrieval accuracy. In this paper, we have performed extensive experimental study and verification. The results suggest that the proposed method works well for lengthy documents with evident spatial distributions of terms.     

基于加权距离的机械设计图像异步检索方法

根据机械设计图像的形状特征,提出一种利用加权距离实现的多特征异步检索方法。首先利用机械设计图像的外接圆距离特征进行初步检索,再结合初步检索结果集的位置计算输入图像和初步检索结果集的加权Hu不变矩特征距离,并据此获得最终的检索结果。实验表明,与单一特征的检索方法相比,该方法在机械设计图像检索中有更高的查准率和查全率。     

一种综合区域匹配的图像检索改进算法

为了能更准确地表达图像信息,提高系统检索性能,提出了一种基于综合区域匹配（IRM）的改进算法。先采用阈值和模糊C-均值相结合的方法分割图像,再采用改进了综合区域距离和重要性因子算法的IRM方法进行图像匹配,并根据图像目标和背景的面积比关系提出“有效距离”概念。实验结果表明,相对于经典算法,改进后算法的平均查准率增加了4.58%。该方法能广泛应用于图像检索系统,具有较大适用性。     

Document Similarity Using a Phrase Indexing Graph Model

Document clustering techniques mostly rely on single term analysis of text, such as the vector space model. To better capture the structure of documents, the underlying data model should be able to represent the phrases in the document as well as single terms. We present a novel data model, the Document Index Graph, which indexes Web documents based on phrases rather than on single terms only. The semistructured Web documents help in identifying potential phrases that when matched with other documents indicate strong similarity between the documents. The Document Index Graph captures this information, and finding significant matching phrases between documents becomes easy and efficient with such model. The model is flexible in that it could revert to a compact representation of the vector space model if we choose not to index phrases. However, using phrase indexing yields more accurate document similarity calculations. The similarity between documents is based on both single term weights and matching phrase weights. The combined similarities are used with standard document clustering techniques to test their effect on the clustering quality. Experimental results show that our phrase-based similarity, combined with single-term similarity measures, gives a more accurate measure of document similarity and thus significantly enhances Web document clustering quality.     

Image retrieval based on index compressed vector quantization

Increased amount of visual data in several applications necessitates content-based image retrieval. Since most of visual data is stored in compressed form, it is crucial to develop indexing techniques for searching images based on their content in compressed form. Therefore, it is desirable to explore image compression techniques with capability of describing image content in compressed form. Vector Quantization (VQ) is a compression scheme that exploits intra-block correlation and image correlation reflects image content, hence VQ is a suitable compression technique for compressed domain image retrieval.This paper introduces a novel indexing scheme for compressed domain image databases based on indices generated from IC-VQ. The proposed scheme extracts image features based on relationship between indices of IC-VQ compressed images. This relationship detects contiguous regions of compressed image based on inter- and intra-block correlation. Experimental results show effectiveness superiority of the new scheme compared to VQ and color-based schemes.     

一种高性能的遥感图像目标快速筛选算法

提出一种基于双向部分Hausdorff距离的特征点匹配方法,目的是提高从遥感图像中自动筛选感兴趣目标的精度和速度。这种方法通过构造图像多分辨率金字塔,按照一定间隔旋转模型,将双向部分Hausdorff距离作为平移矢量的函数来计算并采用多种加速机制等途径,依次确定平移、旋转和尺度变换参数,较好地解决了图像与模型之间存在平移、旋转、尺度变换时对应关系的求解问题。关于实际遥感图像的大量实验表明,该方法具有计算简便快捷、对边缘位置误差稳健、可工作于遮挡、阴影、复杂背景存在的环境中的优点。     

基于统计特征的DCT压缩域纹理图像检索方法

提出了一种基于离散余弦变换（Discrete Cosine Transfrom,DCT）的纹理图像的检索方法.该方法在DCT压缩域,通过直接对DCT系数计算,获得图像纹理的统计特征,并作为检索的依据.理论分析和实验结果都表明,该方法具有很好的检索准确率和效率,并且对于旋转具有不变性.     

基于分支特征点的景象匹配算法

由于传统Hausdorff距离算法对减少非零均值高斯噪声的干扰不明显,且匹配精度不能满足惯导的要求,因而提出了一种改进的算法分支点的加权Hausdorff离（Weiighted Hausdorff Distance,WHD）算法,并给出了权值的求取公式。方法能有效匹配被非高斯噪声污染的图像,提高景象匹配的精度和速度,增强算法的鲁棒性。并对提出的WHD算法与部分的平均距离算法（PMHD）分别作仿真实验进行比较,证明了前者算法的实用性和有效性。     

一种基于鲁棒Hausdorff距离的目标匹配算法

在传统的基于边缘位置的Hausdorff距离匹配的基础上,将边缘的梯度信息引入到距离度量当中,构造了一种新的三维距离函数。在此基础上,提出了一种鲁棒的三维Hausdorff距离及其目标匹配算法,采用粗匹配与精匹配相结合的两步匹配策略有效解决了由距离度量维数增加所导致的算法复杂性增大的问题。实验表明,该算法相对于传统的基于边缘位置的Hausdorff距离目标匹配算法在鲁棒性上有很大的提高。     

基于兴趣本体的文档敏感信息检测方法

随着计算机技术及互联网的高速发展,越来越多的办公主机接入互联网,敏感信息的泄露隐患增多,文档的敏感信息检测显得尤为必要。为了解决传统的查询扩展检测方法查准率和查全率低的问题,构建了监测者关于敏感信息的兴趣本体,提出基于兴趣本体的概念相似度查询扩展算法,并验证了算法的可行性。实验证明该算法有效提高了文档敏感信息检测的查全率和查准率。     

一种基于关键词的中文文档图像检索方法

本文提出了一种基于关键词的中文文档图像检索方法,能在不经OCR(Optical Character Recognition)识别的情况下,直接利用中文字符的图像特征进行关键词检索。首先将文档图像分割成单个中文字符图像,接着对字符图像进行汉字笔画的特征数据提取,然后在特征数据间进行基于WMHD(Weighted Modified Hausdorff Distance)的相似性测量。该方法不受字号的影响,也有一定的抗字体能力,实验证明其具有较高的检索效果。     

Web-based image indexing and retrieval in JPEG compressed domain

As the majority of content-based image retrieval systems operate on full images in pixel domain, decompression is a prerequisite for the retrieval of compressed images. To provide a possible on-line indexing and retrieval technique for those jpg image files, we propose a novel pseudo-pixel extraction algorithm to bridge the gap between the existing image indexing technology, developed in the pixel domain, and the fact that an increasing number of images stored on the Web are already compressed by JPEG at the source. Further, we describe our Web-based image retrieval system, WEBimager, by using the proposed algorithm to provide a prototype visual information system toward automatic management, indexing, and retrieval of compressed images available on the Internet. This provides users with efficient tools to search the Web for compressed images and establish a database or a collection of special images to their interests. Experiments using texture- and colour-based indexing techniques support the idea that the proposed algorithm achieves significantly better results in terms of computing cost than their full decompression or partial decompression counterparts. This technology will help control the explosion of media-rich content by offering users a powerful automated image indexing and retrieval tool for compressed images on the Web.J. Jiang: Contacting author     

综合距离和相关性的图像检索算法

在基于内容的图像检索中,大多数都是采用距离来测试两幅图像的相似性.提出了一种新的计算相关系数的方法并结合这种方法和距离来判断两幅图像的相似性,将其应用于CBIR(content-based image retrieval)系统.在对所提出的算法进行的实验中,用了10 000幅图像来测试了所提出的算法,实验结果表明:在同一个CBIR系统中,引入相关性能够提高图像的检索精度,解决了只用距离来判断两幅图像相似性的不足,对于基于低级特征的图像检索系统是一个很好的改进.     

Ranking consistency for image matching and object retrieval

The goal of object retrieval is to rank a set of images by the similarity of their contents to those of a query image. However, it is difficult to measure image content similarity due to visual changes caused by varying viewpoint and environment. In this paper, we propose a simple, efficient method to more effectively measure content similarity from image measurements. Our method is based on the ranking information available from existing retrieval systems. We observe that images within the set which, when used as queries, yield similar ranking lists are likely to be relevant to each other and vice versa. In our method, ranking consistency is used as a verification method to efficiently refine an existing ranking list, in much the same fashion that spatial verification is employed. The efficiency of our method is achieved by a list-wise min-Hash scheme, which allows rapid calculation of an approximate similarity ranking. Experimental results demonstrate the effectiveness of the proposed framework and its applications.