共查询到19条相似文献,搜索用时 140 毫秒
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基于语义的跨媒体信息检索技术研究 总被引:1,自引:0,他引:1
阐述当前多媒体信息检索技术正从基于内容特征相似性的单一媒体检索发展到基于语义相关性的多种媒体综合检索,实现跨媒体信息检索.提出了一种跨媒体信息检索的系统结构,在分析各种媒体基于语义信息检索的基础上,设计并实现跨媒体搜索引擎及其查询分解策略和检索结果融合方法等,实验结果表明:这种方法能够有效地改善查全率和查准率. 相似文献
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王哲 《微电子学与计算机》2014,(8)
研究语义信息检索方法,提高检索的效率.差异化信息由于特征差异过大,在检索过程中存在较大排异现象,传统的语义信息检索模型针对大差异信息检索过程中,以多轮次检测为主,效率很低.为此,提出一种基于决策树算法的语义信息检索方法.根据多层次解析融合相关理论,计算窗口函数,并且根据窗口函数进行不同层次数据的融合,得到差异信息融合结果.根据上述结果,建立决策树,实现语义信息的检索.实验结果表明,利用改进算法进行语义信息检索,能够提高检索的效率. 相似文献
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刑侦现勘图像数据库是具有保密性高、图像内容罕见等极具行业特色的图像数据库.针对现勘图像内容复杂、目标物体不明确的特点,提出了DCT-DCT波纹理特征,并与HSV颜色直方图特征、GIST特征相融合构成融合特征.与常用的图像特征相比,DCT-DCT波纹理特征能够得到较高的检索效率,而融合特征的平均检索查准率高于构成其本身的三种特征的平均检索查准率.最后,将语义分析技术引入到检索过程中,提出基于检索结果优化的现勘图像检索算法,利用支持向量机(Support Vector Machine,SVM)分类器对查询图像进行语义提取,并对初次检索的结果进行语义分析,根据初检结果中语义类别的占比选择二次检索方案,该算法能在按例查询的基础上进一步提高平均检索查准率. 相似文献
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综合利用颜色和纹理特征的图像检索 总被引:64,自引:0,他引:64
基于特征的图像检索在多媒体数据库管理和多媒体通信传输中得到越来越多的重视。本文介绍了我们设计的分别基于颜色特征和基于纹理特征的两种图像检索算法。在利用单一特征检索的基础上,我们提出了一种综合利用上述两个特征共同进行检索的方法。对真实图像数据库的检索实验表明,综合特征检索要比单一特征检索更符合人的视觉感受要求,因而检索效果更好。 相似文献
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针对单一特征不能很好地表述图像的问题,提出了一种融合多特征的图像检索算法.首先,提取查询图像和图像库中样本图像的GIST(Generalized Search Tree)特征,用欧氏距离衡量图像间的GIST相似度值,根据查询图像的GIST特征在图像库中进行检索,将结果按相似度进行排序;然后,提取查询图像和返回结果中前k幅图像的尺度不变特征变换(SIFT)特征,使用BBF(Best Bin First)算法进行特征匹配;最后,通过特征点匹配点对数排序并返回检索结果.实验在改进的Corel1000数据集上进行,与传统的单特征图像检索算法对比,提出的图像检索算法不仅提高了检索准确率,而且获得了较好的检索效率. 相似文献
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王丽艳 《微电子学与计算机》2013,30(2)
针对单一特征图像自动识别算法存在识别结果不稳定和识别正确率低等缺陷,提出一种基于证据理论和改进神经网络相融合的图像自动识别算法.首先提取能反映图像类别信息的颜色和纹理特征,然后采用RBF神经网络对单一特征进行初步识别,识别结果作作为证据,最后采用证据理论对初步识别结果进行决策融合处理,得到图像最终识别结果.仿真测试结果表明,该算法的平均识别正确率达到92.29%,相对于单一特征识别算法,图像识别结果的可靠性和正确率得到了大幅提高,具有较好的应用前景. 相似文献
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Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning 总被引:9,自引:0,他引:9
Zhong Su Hongjiang Zhang Li S. Shaoping Ma 《IEEE transactions on image processing》2003,12(8):924-937
Research has been devoted in the past few years to relevance feedback as an effective solution to improve performance of content-based image retrieval (CBIR). In this paper, we propose a new feedback approach with progressive learning capability combined with a novel method for the feature subspace extraction. The proposed approach is based on a Bayesian classifier and treats positive and negative feedback examples with different strategies. Positive examples are used to estimate a Gaussian distribution that represents the desired images for a given query; while the negative examples are used to modify the ranking of the retrieved candidates. In addition, feature subspace is extracted and updated during the feedback process using a principal component analysis (PCA) technique and based on user's feedback. That is, in addition to reducing the dimensionality of feature spaces, a proper subspace for each type of features is obtained in the feedback process to further improve the retrieval accuracy. Experiments demonstrate that the proposed method increases the retrieval speed, reduces the required memory and improves the retrieval accuracy significantly. 相似文献
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We present a relevance feedback approach based on multi‐class support vector machine (SVM) learning and cluster‐merging which can significantly improve the retrieval performance in region‐based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low‐level features and high‐level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re‐clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two‐class SVM and multi‐class relevance feedback methods. 相似文献
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Wei Jiang Guihua Er Qionghai Dai Jinwei Gu 《IEEE transactions on image processing》2006,15(3):702-712
Content-based image retrieval (CBIR) has been more and more important in the last decade, and the gap between high-level semantic concepts and low-level visual features hinders further performance improvement. The problem of online feature selection is critical to really bridge this gap. In this paper, we investigate online feature selection in the relevance feedback learning process to improve the retrieval performance of the region-based image retrieval system. Our contributions are mainly in three areas. 1) A novel feature selection criterion is proposed, which is based on the psychological similarity between the positive and negative training sets. 2) An effective online feature selection algorithm is implemented in a boosting manner to select the most representative features for the current query concept and combine classifiers constructed over the selected features to retrieve images. 3) To apply the proposed feature selection method in region-based image retrieval systems, we propose a novel region-based representation to describe images in a uniform feature space with real-valued fuzzy features. Our system is suitable for online relevance feedback learning in CBIR by meeting the three requirements: learning with small size training set, the intrinsic asymmetry property of training samples, and the fast response requirement. Extensive experiments, including comparisons with many state-of-the-arts, show the effectiveness of our algorithm in improving the retrieval performance and saving the processing time. 相似文献
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Combining positive and negative examples in relevance feedback for content-based image retrieval 总被引:2,自引:0,他引:2
M. L. Kherfi D. Ziou A. Bernardi 《Journal of Visual Communication and Image Representation》2003,14(4):428-457
In this paper, we address some issues related to the combination of positive and negative examples to improve the efficiency of image retrieval. We start by analyzing the relevance of the negative example and how it can be interpreted and utilized to mitigate certain problems in image retrieval, such as noise, miss, the page zero problem and feature selection. Then we propose a new relevance feedback approach that uses the positive example (PE) to perform generalization and the negative example (NE) to perform specialization. In this approach, a query containing both PE and NE is processed in two steps. The first step considers the PE alone, in order to reduce the set of images participating in retrieval to a more homogeneous subset. Then, the second step considers both PE and NE and acts on the images retained in the first step. Mathematically, relevance feedback is formulated as an optimization of the intra and inter variances of the PE and NE. The proposed relevance feedback algorithm was implemented in our image retrieval system, which we tested on a collection of more than 10,000 images. The experimental results show how the NE as considered in our model can contribute in improving the relevance of the images retrieved. 相似文献
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为自动调节当前检索任务以使最终查询结果朝着有利于用户要求的方向发展,提出一种基于组合特征双重加权的相关反馈算法。将图像检索中初始权重的设定过程作为优化问题,利用量子遗传算法求取全局最优解,作为图像检索过程中各特征初始权重的加权值;另外,在组合特征权重动态调节的过程中,将灰色关联分析理论中的灰关联度作为特征权重的估计值,同时将反馈结果中每幅图像的评价都考虑到灰色关联分析的计算中,从而来估计不同特征在检索中的相对重要性。实验结果表明,本文算法能够达到精炼检索结果的目的,大幅提高检索全面性和检索准确度。 相似文献
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图像检索是医学图像辅助诊断的基础,为了提高医学图像检索的正确率,提出一种流形学习和相关反馈相融合的医学图像检索算法(LLE-MF)。首先根据方块编码的思想提取颜色分量的信息熵,并利用邻域灰度共生矩阵提取纹理特征;然后采用非线性流形学习对颜色和纹理特征进行组合、降维处理,并采用欧式距离相似度量模型对图像初步进行检索,最后最小二乘支持向量机对初步检索结果进行相关反馈,并进行仿真测试。结果表明,相对于其它医学检索算法,LLE-MF不仅提高了医学图像的检索准确率,同时提高了医学图像的检索效率,可以准确地找到用户所需的图像. 相似文献
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显著性检测是计算机视觉的一项基础问题,广泛地用于注视点预测、目标检测、场景分类等视觉任务当中.为提升多特征条件下图像的显著性检测精度,以显著图的联合概率分布为基础,结合先验知识,设计一种概率框架下的多特征显著性检测算法.首先分析了单一特征显著性检测的潜在缺陷,继而推导出多特征下显著图的联合概率分布;然后根据显著图的稀有性,稀疏性,紧凑性与中心先验推导出显著图的先验分布,并使用正态分布假设简化了显著图的条件分布;随后根据显著图的联合概率分布得到其极大后验估计,并基于多阈值假设构建了分布参数的有监督学习模型.数据集实验表明:相比于精度最高的单一特征显著性检测方法,多特征算法在有监督和启发式方法下的平均误差降低了6.98%和6.81%,平均F-measure提高了1.19%和1.16%;单幅图像的多特征融合耗时仅为11.8ms.算法精度较高,实时性好,且可根据不同任务选择所需的特征类别与先验信息,能够满足多特征显著性检测的性能要求. 相似文献
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A Unified Relevance Feedback Framework for Web Image Retrieval 总被引:1,自引:0,他引:1
《IEEE transactions on image processing》2009,18(6):1350-1357