基于H.264视频压缩域运动矢量的目标跟踪

目前,目标跟踪算法大多在像素域进行,即先将视频解码成图像,再用像素域的方法计算,但像素域算法复杂度高、计算量大。在视频编码过程中,产生了对目标跟踪有利的信息,如运动矢量(MV)和DCT系数,而且直接利用压缩域信息进行目标跟踪可减少解码的时间。文章提出了一种基于像素运动矢量的H.264压缩域跟踪算法,与宏块运动矢量算法相比,该算法显著提高了跟踪准确率。     

压缩域DCT系数对图像视频检索影响的研究

通过对图像在原始数据域和压缩数据域上的表现差异性进行统计分析，进一步研究了基于DCT变换的图像视频压缩域部分解码DCT信息，并运用于MPEG4的编解码的程序实现。随后提出了一种提高压缩域图像视频检索效率的方法，进而针对Intel X86处理器平台改进了DC 2AC的快速算法。     

快速小波熵在图像检索中的应用

图像检索技术是多媒体应用中的关键技术,现有的基于内容的图像检索技术大都是基于非压缩域的.随着各种压缩标准的推出与普及,人们开始研究直接在压缩域的图像检索技术.由于小波变换在图像压缩中的应用,文献中提出了基于小波直方图的图像检索技术,但该技术的计算复杂性高,检索速度较慢.提出了快速小波熵的图像检索技术以减少小波直方图技术的计算复杂性并提高了检索速度.     

视频检索中的镜头边界检测

镜头边界检测是视频检索的关键技术。文中介绍了目前镜头边界检测国内外的研究成果。各检测方法的特点及其不足之处,并指出了发展方向。检测手段主要集中在像素域和压缩域中。其中在像素域中的检测方法主要包括：像素亮度匹配、直方图比较、基于块的技术、二次比较、基于运动矢量的技术、基于网络的技术、基于镶嵌的技术。压缩域中的方法主要包括：利用ＤＣＴ系数进行匹配、利用矢量量化的方法、利用子带分解的方法、利用运动矢量的     

视频检索中的边界检测算法

从视频检索技术的发展背景出发，重点介绍了视频检索技术中镜头的边界检测技术。分别介绍了基于解压的全图像序列的算法，基于压缩视频的算法以及基于确定变换模型的算法三类算法。最后，对基于内容的视频检索提出一些值得进一步研究的问题。     

面向检索的压缩域空时视频目标检测与分割

面向语义视频检索,提出一种压缩域的目标分割新算法。它直接基于压缩码流中运动矢量和DCT系数,经过运动检测、矢量分水岭分割、目标融合与修正、后处理与跟踪等步骤提取空时视频目标。整个过程主要基于压缩域进行,无需视频码流的完全解码。对不同测试序列的实验测试结果显示算法能基于压缩域提取较为精确的空时视频目标,并具有较好的鲁棒性。     

基于距离度量学习的DCT域JPEG图像检索

由于特征有限,传统基于欧式距离的压缩域检索性能并不理想。本文引入距离度量学习技术,研究压缩域图像检索,提出了一种基于距离度量学习的离散余弦变换（DCT）域联合图像专家小组（JPEG）图像检索方法。首先,提出了一种更有效的 DCT 域特征提取方法;其次,运用距离度量学习技术训练出一个更加有效的度量矩阵进行检索。在 Corel5000上的图像检索实验表明,新方法有效提高了检索准确度。     

视频分割技术概观

随着数字视频的飞速增长。对视频内容快速而有效的检索技术变得越来越重要。本概观了基于内容的视频分割技术。主要包括非压缩域和压缩域两个主要方面的镜头变换检测算法。     

一种分形域基于内容的图像检索方法

基于内容的图像检索是多媒体、网络通信及计算机等应用研究领域的一项关键技术。该文提出了一种在分形压缩域直接进行基于内容的图像检索方法。该方法不需要对查询图像进行分形变换，因此可以提高检索速度，降低检索复杂度。仿真结果表明，使用该文提出的方法，能够有效地进行分形域基于内容的图像检索，比较大幅度地降低了检索时间，优于试验中其他3种方法。     

基于重组DCT系数子带能量直方图的图像检索

现在许多图像采用JPEG格式存储,检索这些图像通常要先解压缩,然后提取基于像素域的特征矢量进行图像检索。己有文献提出直接在DCT域进行图像检索的方法,这样可以降低检索的时间复杂度。本文提出对JPEG图像的DCT系数利用多分辨率小波变换的形式进行重组,对整个数据库中所有图像的DCT系数重组得到的若干子带,分别建立子带能量直方图,而后采用Morton顺序建立每幅图像的索引,并采用变形B树结构组织图像数据库用于图像检索。     

An Evaluation of the Effectiveness of Image Features for Image Retrieval

Retrieval effectiveness in image databases depends significantly on the features and the distance model utilized to evaluate the similarity of the images. Features must be extracted from images and stored in the database. Since the features should be stored at the time of data entry, it is extremely important to determine which features ensure the best retrieval performances. In this paper a comparison of the most widespread automatic indexing techniques and their performances is presented. The image reference set, necessary for performance comparison, is obtained by including in the database frames extracted from video shots. Frames extracted from a shot are different but have the same semantic content. One of these frames is utilized as an example in a query; the indexing effectiveness is assessed from the frames retrieved. The most relevant features prove to be the angular spectrum, the Hough transform, and the color histogram, followed by local features such as local luminance pattern directionality.     

Content-Based Image Retrieval Using Moments of Local Ternary Pattern

Due to the availability of large number of digital images, development of an efficient content-based indexing and retrieval method is required. Also, the emergence of smartphones and modern PDAs has further substantiated the need of such systems. This paper proposes a combination of Local Ternary Pattern (LTP) and moments for Content-Based Image Retrieval. Image is divided into blocks of equal size and LTP codes of each block are computed. Geometric moments of LTP codes of each block are computed followed by computation of distance between moments of LTP codes of query and database images. Then, the threshold using distance values is applied to retrieve images similar to the query image. Performance of the proposed method is compared with other state-of-the-art methods on the basis of results obtained on Corel-1,000 database. The comparison shows that the proposed method gives better results in terms of precision and recall as compared to other state-of-the-art image retrieval methods.     

Perceptual Image Indexing and Retrieval

We describe a perceptual approach to generating features for use in indexing and retrieving images from an image database. Salient regions that immediately attract the eye are large color regions that usually dominate an image. Features derived from these will allow search for images that are similar perceptually. We compute color features and Gabor color texture features on regions obtained from a multiscale representation of the image, generated by a multiband smoothing algorithm based on human psychophysical measurements of color appearance. The combined feature vector is then used for indexing all salient regions of an image. For retrieval, those images are selected that contain more similar regions to the query image by using a multipass retrieval and ranking mechanism. Matches are found using the L₂ metric. The results demonstrate that the proposed method performs very well.     

使用拼贴误差直方图的分形图像检索

拼贴误差是值域块与最匹配的定义域块相似性的一个度量,然而很多基于分形编码参数的分形图像检索技术却忽略了拼贴误差。该文使用拼贴误差直方图作为检索条件,来判定图像的相似度。对有200多张纹理图像的数据库的实验显示,该方法不仅减少了计算的复杂度,而且也有一定的检索准确率。     

Indexing and retrieval of images by spatial constraints

Many multimedia applications require retrieval of spatially similar images against a given query image. Existing work on image retrieval and indexing either requires extensive low-level computations or elaborate human interaction. In this paper, we introduce a new symbolic image representation technique to eliminate repetitive tasks of image understanding and object processing. Our symbolic image representation scheme is based on the concept of hierarchical decomposition of image space into spatial arrangements of features while preserving the spatial relationships among the image objects. Quadtrees are used to manage the decomposition hierarchy and play an important role in defining the similarity measure. This scheme is incremental in nature, can be adopted to accommodate varying levels of details in a wide range of application domains, and provides geometric variance independence. While ensuring that there are no false negatives, our approach also discriminates against non-matching entities by eliminating them as soon as possible, during the coarser matching phases. A hierarchical indexing scheme based on the concept of image signatures and efficient quadtree matching has been devised. Each level of the hierarchy tends to reduce the search space, allowing more involved comparisons only for potentially matching candidate database images. For a given query image, a facility is provided to rank-order the retrieved spatially similar images from the image database for subsequent browsing and selection by the user.     

MRI and CT image indexing and retrieval using local mesh peak valley edge patterns

In this paper, a new pattern based feature, local mesh peak valley edge pattern (LMePVEP) is proposed for biomedical image indexing and retrieval. The standard LBP extracts the gray scale relationship between the center pixel and its surrounding neighbors in an image. Whereas the proposed method extracts the gray scale relationship among the neighbors for a given center pixel in an image. The relations among the neighbors are peak/valley edges which are obtained by performing the first-order derivative. The performance of the proposed method (LMePVEP) is tested by conducting two experiments on two benchmark biomedical databases. Further, it is mentioned that the databases used for experiments are OASIS−MRI database which is the magnetic resonance imaging (MRI) database and VIA/I–ELCAP-CT database which includes region of interest computer tomography (CT) images. The results after being investigated show a significant improvement in terms average retrieval precision (ARP) and average retrieval rate (ARR) as compared to LBP and LBP variant features.     

基于“点到面”策略的图像检索

图像检索是计算机视觉领域的一个重要分支。其主要目的是从图像数据库中找出与查询图像相似的语义图像。传统的图像检索方法是在查询图像和数据库图像之间进行“点到点”检索。但是,单个查询图像包含的类别提示较少,即类别信息较弱,使得检索结果并不理想。为了解决这个问题,本文提出了一种基于“点到面”的类别检索策略来扩展一个图像（点）到一个图像类别（面）,这意味着从单个查询图像到整个图像类别的语义扩展。该方法挖掘了查询图像的类别信息。在两个常用的数据集上对所提出方法的性能进行了评估。实验表明,该方法可以显著提高图像检索的性能。      

An efficient signature representation for retrieval of spatially similar images

Multimedia applications involving image retrieval demand fast and efficient response. Efficiency of search and retrieval of information in a database system is index dependent. Generally, a two-level indexing scheme in an image database can help to reduce the search space against a given query image. In such type of indexing scheme, the first level is required to significantly reduce the search space for second stage of comparisons and must be computationally efficient. It is also required to guarantee that no false negatives may result. The second level of indexing involves more detailed analysis and comparison of potentially relevant images. In this paper, we present an efficient signature representation scheme for first level of a two-level image indexing scheme that is based on hierarchical decomposition of image space into spatial arrangement of image features. Experimental results demonstrate that our signature representation scheme results in fewer number of matching signatures in the first level and significantly improves the overall computational time. As this scheme relies on corner points as the salient feature points in an image to describe its contents, we also compare results using several different contemporary corner detection methods. Further, we formally prove that the proposed signature representation scheme not only results in fewer number of signatures but also does not result in any false negative.