一种基于分布式LSH的海量视频快速检索方法

针对大规模视频数据库,提出一种快速的视频相似性检索方法.该方法采用基于内存约束的分布式哈希结构,称为MD-LSH,有效地解决了快速与可扩展性问题.首先从视频序列中提取出特征向量集合,然后使用MD-LSH对高维特征向量建立索引,根据返回的相似帧集合对相关的视频进行相似度计算,最后排序得到相似视频的查询结果.实验表明,该方法对大规模视频的快速检索是非常有效的.     

基于颜色和纹理特征的彩色图像检索方法

由于单一特征不足以准确地描述图像的特征,文章提出了一种新的彩色图像检索方法,该方法在对图像进行分块的基础上,分别计算图像R、G、B三通道的均值和方差,获取了图像的颜色统计特征;然后通过小波包变换提取纹理特征。在进行图像间的相似性测量时,为了结合不同的特征进行全局的相似性检索,还采用Guassian模型对不同特征间的距离进行了归一化处理。仿真结果表明,提出的方法具有良好的检索效果。     

基于人脸检测的视频检索技术研究

为了提高视频检索的效率和准确率,本文提出了一种基于人脸检测技术的视频检索方法。该方法通过人脸检测技术将视频每一帧中的人脸与双手检测出来,然后通过建立点线连接来创建人脸与双手的拓扑结构;最后通过拓扑结构的连续变化特点来识别视频中人物的动作特征,从而达到视频检索的目的。实验表明,该方法检索速度较快且识别度较高,有广泛的应用前景。     

基于图像识别的收视率统计方法与研究

通过图像识别技术,实现收视率的统计任务。根据每个电视台都有自己的台标,通过一定的视频技术,得到视频帧。然后通过截图技术,获得台标图片,经过预处理之后与台标图库中已有台标的特征矩阵相对比。通过匹配特征矩阵的方法进行初级检索,将不相似的台标图像排除;之后用特征点匹配算法进行高级检索,获得相似台标,得到电台信息,完成对观众收看节目的统计工作。     

基于特征的图像视频信息检索研究

图像及视频检索是当前计算机视觉、多媒体数据库管理等研究领域的热点之一,就此介绍了该研究领域的现状。对于静态图像,介绍了基于颜色、纹理、形状等图像特征信息的检索;对于视频序列,则介绍特征提取、相似性测量及其检索策略。最后指出了该研究领域存在的难点及今后的工作。     

专利的相关性检索与集成应用研究

摘要：阐述了专利分析技术和专业应用系统的现状和特点,提出通过专利模型树来描述专利文档,并以专利模型树为基础,建立了基于向量空间模型的专利分类方法和专利相似性检索方法。基于上述方法,在工作流管理系统中集成专利管理系统,建立了集成框架,开发了集成系统,实现了在企业工作流程中的每个工作单元与专利相似性检索模块的集成。最后,在某企业的电缓速器设计的工作流系统中得以应用。     

基于协同过滤的联机公共目录查询系统个性化检索结果排序研究

提出并实现基于协同过滤的联机公共目录查询系统个性化检索结果排序方法.该方法构建同趣读者引擎,计算读者间的相似性及最近邻居.当读者通过OPAC进行检索时,系统将其最近邻居的相关借阅书目在结果中优先显示,以实现个性化检索的目的.该方法显著提高了检索的查准率.     

新闻视频帧中的标题字幕探测

新闻视频中出现的字幕往往包含大量的信息．是视频分析的重要语义线索,探测并识别出来的文字可以为基于内容的视频检索提供索引。通过对新闻字幕的特点进行分析,文章提出了一种基于边缘特征的新闻字幕探测方法。算法首先对图像进行灰度变换,然后对灰度图进行边缘检测,最后进行字幕区域的探测与合并。通过对不同的新闻视频帧进行实验,获得了平均96．8％的探测准确率。实验结果表明,文章提出的方法能够较好的完成新闻字幕检测的任务。     

视频大数据技术助力智慧城市建设

视频大数据技术能够快速处理非结构化视频数据,并实现高效地存储、检索、分析和统计,应用于当前智慧城市建设的交通、安全等诸多领域,未来将会更好地助力智慧城市建设。     

基于关键帧的视频底层特征提取方法分析

良好的底层特征提取能大大提高视频检索的效率,是基于内容的视频检索中重要的一环。本文从研究的目的与意义出发,主要对颜色、纹理及形状特征的提取进行了分析,并对其中涉及到的方式方法进行了实验分析。     

基于领域限定网络检索的术语相似度计算

在分析基于搜索引擎的术语相似度算法基础上,设计并实现了基于领域限定网络检索的术语相似度算法,通过将语义上下文和领域上下文引入检索式构造过程,有效提升了特定领域术语相似度计算效果。     

基于改进关联规则的本体关系获取研究

文章提出一种改进的关联规则方法,用于抽取文本中的非分类关系。首先利用基于上下文的术语相似度获取方法得到术语间的相似度权重,再通过加入谓语动词的关联规则算法计算,结合搜索引擎技术得到候选关系对集合,并通过置信度和支持度的对比分析,抽取最终的非分类关系结果,最后对测试数据进行实验,并对结果进行分析。     

粒计算及其在数据挖掘中的应用

本文分绍了信息粒及粒计算的概念．建立了粒与二进制数的对应关系。定义了粒计算与二进制教计算的相似性和互换性．因此用粒计算方法作数据挖掘被转换为用二进制计算方法来约简数据和提取关联规则。这样为我们的决策提供了速度快、效率高的挖掘方法。     

Near-duplicate video detection featuring coupled temporal and perceptual visual structures and logical inference based matching

We propose in this paper an architecture for near-duplicate video detection based on: (i) index and query signature based structures integrating temporal and perceptual visual features and (ii) a matching framework computing the logical inference between index and query documents. As far as indexing is concerned, instead of concatenating low-level visual features in high-dimensional spaces which results in curse of dimensionality and redundancy issues, we adopt a perceptual symbolic representation based on color and texture concepts. For matching, we propose to instantiate a retrieval model based on logical inference through the coupling of an N-gram sliding window process and theoretically-sound lattice-based structures. The techniques we cover are robust and insensitive to general video editing and/or degradation, making it ideal for re-broadcasted video search. Experiments are carried out on large quantities of video data collected from the TRECVID 02, 03 and 04 collections and real-world video broadcasts recorded from two German TV stations. An empirical comparison over two state-of-the-art dynamic programming techniques is encouraging and demonstrates the advantage and feasibility of our method.     

云环境下制造资源和服务需求相似度计算

针对基于云环境下制造资源和服务需求的本体相似度计算的研究情况,提出一种改进的混合语义相似度算法。该算法针对国内云制造平台下制造资源和服务需求的特点,将中文短语名称相似度和基于属性相似度相结合,在保证相似度结果准确的前提下,将相似度计算方法拓展到制造资源和服务需求之间。最后以某云制造平台的部分制造资源和服务需求为例,验证该方法的有效性。     

一种基于特征词聚类的文本分类方法

本文阐述了一种基于特征词聚类的降维方式,其主要思想就是把词在文本中的出现看成一个事件,先通过搜索算法计算每一个特征词的分布,合并对分类有相似作用的特征词,从而起到了特征降维的作用。最后通过实验测试分析,提出了一种改进的、考虑全局簇信息的相似度计算公式,将其应用到文本分类中,实验表明提高了文本分类的精度。     

改进的ASM灰度图像目标定位技术

首先介绍灰度模型的目标定位算法,在该算法基础上提出了一种改进的算法,将主动形状模型的灰度图像中目标定位算法应用到视频图像中,同时针对处理结果进行科学评价。实验结果证明了本文提出算法的可行性和高效性。     

XMage: An image retrieval method based on partial similarity

XMage is introduced in this paper as a method for partial similarity searching in image databases. Region-based image retrieval is a method of retrieving partially similar images. It has been proposed as a way to accurately process queries in an image database. In region-based image retrieval, region matching is indispensable for computing the partial similarity between two images because the query processing is based upon regions instead of the entire image. A naive method of region matching is a sequential comparison between regions, which causes severe overhead and deteriorates the performance of query processing. In this paper, a new image contents representation, called Condensed eXtended Histogram (CXHistogram), is presented in conjunction with a well-defined distance function CXSim() on the CX-Histogram. The CXSim() is a new image-to-image similarity measure to compute the partial similarity between two images. It achieves the effect of comparing regions of two images by simply comparing the two images. The CXSim() reduces query space by pruning irrelevant images, and it is used as a filtering function before sequential scanning. Extensive experiments were performed on real image data to evaluate XMage. It provides a significant pruning of irrelevant images with no false dismissals. As a consequence, it achieves up to 5.9-fold speed-up in search over the R*-tree search followed by sequential scanning.     

Video retrieval framework based on color co-occurrence feature of adaptive low rank extracted keyframes and graph pattern matching

In recent times, exploration of multimedia required ever increasing demand and application for intelligent video retrieval from repositories. This paper presents an efficient video retrieval framework by employing the effective singular value decomposition and computationally low complex ordered dither block truncation coding to extract simple, compact, and well discriminative Color Co-occurrence Feature (CCF). In this context, the occurrence probability of a video frame pixel in the neighborhood is employed to formulate this specific and distinct feature. Moreover, we applied a new adaptive low rank thresholding based on energy concentricity, transposition, and replacement invariance characteristics to formulate a unified fast shot boundary detection approach to solve the protuberant bottleneck problem for real-time cut and gradual transition that eventually contributes for effective keyframes extraction. Therefore, we can assert that the keyframes are distinct and discriminative to represent the whole video content. For effective indexing and retrieval, it is imperative to formulate similarity score evaluator for the encapsulated contextual video information with substantial temporal consistency, least computation, and post-processing. Therefore, we introduced graph-based pattern matching for video retrieval with an aim to sustain temporal consistency, accuracy and time overhead. Experimental results signify that the proposed method on average provides 7.40% and 17.91% better retrieval accuracy and 23.21% and 20.44% faster than the recent state-of-the-art methods for UCF11 and HMDB51 standard video dataset, respectively.     

Supporting exploratory video retrieval tasks with grouping and recommendation

In this paper, we present ViGOR (Video Grouping, Organisation and Recommendation), an exploratory video retrieval system. Exploratory video retrieval tasks are hampered by the lack of semantics associated to video and the overwhelming amount of video items stored in these types of collections (e.g. YouTube, MSN video, etc.). In order to help facilitate these exploratory video search tasks we present a system that utilises two complementary approaches: the first a new search paradigm that allows the semantic grouping of videos and the second the exploitation of past usage history in order to provide video recommendations. We present two types of recommendation techniques adapted to the grouping search paradigm: the first is a global recommendation, which couples the multi-faceted nature of explorative video retrieval tasks with the current user need of information in order to provide recommendations, and second is a local recommendation, which exploits the organisational features of ViGOR in order to provide more localised recommendations based on a specific aspect of the user task. Two user evaluations were carried out in order to (1) validate the new search paradigm provided by ViGOR, characterised by the grouping functionalities and (2) evaluate the usefulness of the proposed recommendation approaches when integrated into ViGOR. The results of our evaluations show (1) that the grouping, organisational and recommendation functionalities can result in an improvement in the users’ search performance without adversely impacting their perceptions of the system and (2) that both recommendation approaches are relevant to the users at different stages of their search, showing the importance of using multi-faceted recommendations for video retrieval systems and also illustrating the many uses of collaborative recommendations for exploratory video search tasks.