基于方块编码的图像特征提取及检索算法

提出了一种基于方块编码的图像检索算法.首先将图像分成互不重叠的子图像块,根据图像块中各像素间的色差,利用方块编码的思想对这些子图像进行编码,然后根据人眼的视觉特性来定义图像的关键块,最后借助于基于关键字的文本检索技术进行图像检索.同时,考虑到不同类型的关键块在表征图像内容时重要程度的不同而赋予其不同的权值.实验结果表明本文算法在图像的相似性检索时是有效的,并具有较高的检索效率.     

基于内容图像检索的关键技术探析

围绕基于内容图像检索中的关键技术,系统地研究图像各种特征的提取技术。主要包括图像的颜色特征、形状特征以及颜色的空间分布特征以及相关反馈技术,介绍多特征提取的特点,并对CBIR技术的未来发展做展望。     

基于内容的图像检索技术

基于内容的图像检索是一种综合集成技术。通过分析图像内容,如颜色、纹理等,建立特征索引,并存储在特征库中。查询时,只要把自己对图像的模糊印象描述出来,就可以在大容量图像库中找到想要的图像。     

基于内容的图像检索技术综述

本文首先介绍了图像检索技术的发展历程,然后介绍了基于内容的图像检索技术的现状,最后给出了图像检索技术的发展方向。     

基于颜色和形状的图像检索

提出一种新的基于颜色和形状的图像检索方法.将彩色图像转换到HSV空间将彩色图像转换到HSV空间,同时将检索图像分块,分别计算每个子块图像的颜色信息熵和中心矩;同时利用Harris算法提取检索图像的兴趣点,计算兴趣点环形空间分布的颜色信息熵和中心矩,以此表征图像的形状特征.针对颜色和形状特征进行高斯归一化,分别计算图像颜色和形状的相似度.最后,利用颜色和形状相似度的加权和进行图像检索.试验结果表明本文算法比CCSI算法和SCH算法具有较高的检索率.     

基于空间分布的图像检索算法研究

针对利用颜色直方图进行图像检索时存在的问题,提出了一种基于空间分布特征的图像检索算法.首先将图像分解为8个位平面,并采用其中对表征图像结构特征有意义的4个位平面的分布特征对图像内容进行描述,同时,为避免图像中像素灰度值的微小变化对位平面的影响,又提出采用位平面的格林码表示方法.考虑到位平面之间的相关性,设计了权值矩阵,采用马氏距离进行相似性度量.实验结果表明,该算法利用位平面分解的方法来提取颜色的空间分布信息,可避免颜色量化引起的误检问题,而且提取的特征矢量维数较低,具有较高的检索率.     

JSEG图像分割算法改进

图像分割是一种重要的和关键的图像分析技术,目的是把图像分成各具特性的区域并提取感兴趣的部分。在基于内容的图像检索中,要对图像特征进行提取,图像分割是其中一个重要的步骤。现在图像分割算法已经有很多,为提高图像检索效率,对JSEG算法进行改进,实验证明该算法有效提高图像检索效率。     

基于内容的医学图像检索技术研究

对基于内容的图像检索方法进行了介绍,并就其设计思想和方法进行了分析。对具体实现的核心技术进行了一些总结和分析。     

基于内容的检索技术在影像档案管理中的应用

针对档案管理中心数字图像或视频的检索问题，阅述基于内容检索技术的核心方法，给出在影像档案管理中构建基于内容检索系统的实现方案，探讨以机器学习方法为内核的相关反馈算法的基本原理，最后简要介绍国际上通用的多媒体内容描述接口（MPEG-71）标准的概念及其应用。     

用FSVM-MIL算法实现图像检索

针对基于对象的图像检索问题,利用模糊支持向量机(FSVM)提出了一种新的多示例学习算法——FSVM-MIL算法.在标准的多示例学习问题中,一个包被标为正包,则它至少包含一个示例是正的,否则被标为负包.FSVM-MIL算法将图像当作包,分割后的区域当作包中的示例,若图像包含有感兴趣对象,则对应的包标为正,否则标为负,因为正包中的示例不全是正的,概念标号存在模糊性,本文利用多样性密度方法寻找概念点,根据noisy-or概率模型定义了模糊隶属度函数,为正包中的示例赋予不同的模糊因子,用FSVM求解多示例学习问题.在SIVAL图像集进行对比实验,结果表明FSVM-MIL算法是有效的且性能不亚于其它同类方法.     

A colour image retrieval scheme based on Z-scanning technique

Abstract

This paper proposes a new colour image retrieval scheme using Z-scanning technique for content-based image retrieval (CBIR). In recent years, the CBIR is a popular research topic for image retrieval. This paper proposes a scheme which employs the Z-scanning technique to extract directional intensity features for measuring the similarity between query and database images. In the multiple channel images, each colour channel can be processed individually or combined into a grey channel Y. In order to extract the features by Z-scanning technique from all images, each channel of all images must be divided into several N×N blocks. In each block, F pairs of pixels are scanned by a ‘Z’ direction to obtain the texture features. Each colour channel can be obtained an M×M Z-scanning co-occurrence matrix (ZSCM) for storing the probability of each relationship of all closest blocks. At the similarity measure stage, the ZSCMs of query image and database images are compared to measure their similarity. The experimental results show that the proposed scheme is beneficial for image retrieval when the images include the same texture or object. On the other hand, the proposed scheme also can get better retrieval results and more efficiency than colour correlogram (CC) technique for colour texture images. Another technique uses motif co-occurrence matrix (MCM) as the feature in similarity measurement. The experimental results show the proposed ZSCM can get better retrieval results and higher recall and precision values than the CC and MCM techniques for public image databases.     

A content-based image retrieval system based on object-moment feature

The last decade has witnessed great interest in research on content-based image retrieval (CBIR). In 2009, Lin et al. proposed a smart CBIR system based on colour and texture feature. Their system has a high detection rate except the cases where image objects have similar shapes. To enhance the detection rate a shape-based image feature called object-moment is proposed in this paper. Object-moment uses the moment of force to compute the object edge feature by calculating the distance from each edge pixel to the axis, and adding them up as a feature. Besides, we integrate the colour features (NSOM, CSOM) and the texture features (CCM, DBPSP) to enhance image detection rate and simplify computation of image retrieval. A series of analyses and comparisons are performed in our experiments to demonstrate that our proposed method improves the retrieval accuracy significantly.     

支持向量像素抽样的快速图像匹配方法

提出了一种新的支持向量(SupportVectorMachines,SVM)回归的快速图像匹配方法。该方法将匹配模板图像中每个像素的位置坐标和灰度信息作为训练样本,通过选择合适的模型参数,进行SVM回归训练,获得少量的支持向量。依据SVM位置坐标对模板图像进行像素抽样,实现匹配数据的有效压缩。定义了图像支持特征向量,用少量的特征数据描述整幅图像变化的结构信息,保证了匹配数据的置信度。采用相关系数作为相似性测度,实现互相关匹配。实验结果显示,在一幅100×100的光学图像中提取85个支持特征向量点作为匹配数据,匹配概率可达到100%,匹配速度比传统相关匹配方法快近四倍。     

A dynamic threshold-based local mesh ternary pattern technique for biomedical image retrieval

Many content-based image retrieval techniques like local binary pattern (LBP), local ternary pattern (LTP), local mesh peak valley edge pattern (LMePVEP), local mesh ternary pattern (LMeTerP), etc. extract texture features of an image for retrieval purposes. These techniques use fixed threshold to encode the input image and selection of such threshold value is not guided, that is, a chosen threshold may not be optimal for all images in the database. Moreover the performance of these texture-based static threshold algorithms also decreases if the input images are noisy. In this paper, a dynamic threshold value-based local mesh ternary pattern method is proposed in which the threshold value is chosen from the neighborhood of a central pixel using median of all pixels. The proposed modification reduces the overall effect of noise component and thereby improves the average retrieval rate (ARR) and average retrieval precision (ARP) of the original technique. The proposed modified technique has been compared with five other image retrieval approaches to prove its worthiness - the original local mesh ternary pattern technique (LMeTerP), a local ternary pattern technique (LTP), a Best ensemble technique, a multi-label classification CNN model and a CNN-based model of the proposed approach using a VIA ELCAP lung database and an Emphysema database. An improvement of 3.92% in ARR and 2.53% in ARP is observed over the basic local mesh ternary pattern method. Further the proposed modification has been combined with CNN concept and its results have also been analyzed.     

基于特征点匹配的图像拼接算法研究

通过查找待拼接两幅图像的对应特征点以确定两幅图像的缝合点，进而确定两者的匹配位置，通过对图像的重叠区域进行平滑过渡处理，实现了待拼接图像的无缝拼接。实验结果表明，文中所提算法合理，取得了良好的效果。     

Digital image watermarking based on VSS in BTC domain

Abstract

An image watermarking model based on visual secret sharing (VSS) is proposed for protection of ownership. In the embedding phase, the watermark is first divided by VSS into two parts: a hiding watermark and a secret watermark. Then only the hiding watermark is embedded into the original image and the secret watermark is reserved for watermark extracting by the owner. In the extracting phase, the hiding watermark is extracted from the watermarked image first and then directly superimposed on the secret watermark to recover the watermark information.     

Optical image encryption using password key based on phase retrieval algorithm

A novel optical image encryption system is proposed using password key based on phase retrieval algorithm (PRA). In the encryption process, a shared image is taken as a symmetric key and the plaintext is encoded into the phase-only mask based on the iterative PRA. The linear relationship between the plaintext and ciphertext is broken using the password key, which can resist the known plaintext attack. The symmetric key and the retrieved phase are imported into the input plane and Fourier plane of 4f system during the decryption, respectively, so as to obtain the plaintext on the CCD. Finally, we analyse the key space of the password key, and the results show that the proposed scheme can resist a brute force attack due to the flexibility of the password key.     

Intensity‐based image matching for regular firearms

This paper reports the digitized image matching of intensity‐based signals generated by the POLIVIEW imaging system for regular firearms. The POLIVIEW imaging system transforms nonlinearized models into linearized models by applying a differential matching method. This paper also discusses the performance of the computer program for determining the peaks of the histograms generated and their threshold segmentation for matching. This method can be used to generate corroborative evidence in solving crimes. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 68–72, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10011