共查询到20条相似文献,搜索用时 234 毫秒
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提出了一种新的利用图像语义词汇表进行图像自动标注与检索的方法.采用混合层次模型在已标注好的训练图像集上计算图像区域类与关键字的联合概率分布,并用生成的模型标注未曾观察过的测试图像集,或用来进行基于语义的图像检索.实验结果表明,该方法在标注、检索精度和效率方面均优于当前其他方法. 相似文献
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图像自动标注的实质是通过对图像视觉特征的分析来提取高层语义关键词用于表示图像的含义,从而使得现有图像检索问题转化为技术已经相当成熟的文本检索问题,在一定程度上解决了基于内容图像检索中存在的语义鸿沟问题.采用t混合模型在已标注好的训练图像集上计算图像区域类与关键字的联合概率分布,在此基础上,对未曾观察过的测试图像集,利用生成的模型根据贝叶斯最小错误概率准则实现自动图像标注.实验结果表明,该方法能有效改善标注结果. 相似文献
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由于图像数据中普遍存在的“语义鸿沟”问题,传统的基于内容的图像检索技术对于数字图书馆中的图像检索往往力不从心。而图像标注能有效地弥补语义的缺失。文中分析了图像语义标注的现状以及存在的问题,提出了基于语义分类的文物语义标注方法。算法首先通过构建一个Bayes语义分类器对待标注图像进行语义分类,进而通过在语义类内部建立基于统计的标注模型,实现了图像的语义标注。在针对文物图像进行标注的实验中,该方法获得了较好的标注准确率和效率。 相似文献
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根据图像低层特征和高级语义间的对应关系,自动进行图像语义标注是目前图像检索系统研究的热点。简要介绍了基于图像语义连接网络的图像检索框架,提出了一种基于该框架的图像自动标注模型。该模型通过积累用户反馈信息,学习并获得图像语义,从而进行自动的图像标注。图像语义及标注可以在与用户交互过程中得到实时更新。还提出了一种词义相关度分析的方法剔除冗余标注词,解决标注误传播的问题。通过在Corel图像集上的对比实验,验证了该方法的有效性。 相似文献
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网络图像语义自动标注是实现对互联网中海量图像管理和检索的有效途径,而自动有效地挖掘图像语义是实现自动语义标注的关键。网络图像的语义蕴含于图像自身,但更多的在于对图像语义起不同作用的各种描述文本,而且随着图像和描述知识的变化,描述文本所描述的图像语义也随之变化。提出了一种基于领域本体和不同描述文本语义权重的自适应学习的语义自动标注方法,该方法从图像的文本特征出发考查它们对图像语义的影响,先通过本体进行有效的语义快速发现与语义扩展,再利用一种加权回归模型对图像语义在其不同类型描述文本上的分布进行自适应的建模,进而实现对网络图像的语义标注。在真实的Wcb数据环境中进行的实验中,该方法的有效性得到了验证。 相似文献
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Jia Li Wang J.Z. 《IEEE transactions on pattern analysis and machine intelligence》2003,25(9):1075-1088
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In this paper, the problem of indexing symbolic images based on spatial similarity is addressed. A model based on modified triangular spatial relationship (TSR) and B-tree is proposed. The model preserves TSR among the components in a symbolic image by the use of quadruples. A Symbolic Image Database (SID) is created through the construction of B-tree, an efficient multilevel indexing structure. A methodology to retrieve similar symbolic images for a given query image is also presented. The presented retrieval model has logarithmic search time complexity. The study made in this work reveals that the model bears various advantages when compared to other existing models and it could be extended towards dynamic databases. An extensive experimentation is conducted on various symbolic images and also on the ORL and YALE face databases. The results of the experimentation conducted have revealed that the proposed scheme outperforms the existing algorithms and is of practical relevance. 相似文献
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Clemens D.T. Jacobs D.W. 《IEEE transactions on pattern analysis and machine intelligence》1991,13(10):1007-1017
Model-based visual recognition systems often match groups of image features to groups of model features to form initial hypotheses, which are then verified. In order to accelerate recognition considerably, the model groups can be arranged in an index space (hashed) offline such that feasible matches are found by indexing into this space. For the case of 2D images and 3D models consisting of point features, bounds on the space required for indexing and on the speedup that such indexing can achieve are demonstrated. It is proved that, even in the absence of image error, each model must be represented by a 2D surface in the index space. This places an unexpected lower bound on the space required to implement indexing and proves that no quantity is invariant for all projections of a model into the image. Theoretical bounds on the speedup achieved by indexing in the presence of image error are also determined, and an implementation of indexing for measuring this speedup empirically is presented. It is found that indexing can produce only a minimal speedup on its own. However, when accompanied by a grouping operation, indexing can provide significant speedups that grow exponentially with the number of features in the groups 相似文献
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3D Object Recognition and Pose with Relational Indexing 总被引:1,自引:0,他引:1
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Modeling semantic aspects for cross-media image indexing 总被引:3,自引:0,他引:3
Monay F Gatica-Perez D 《IEEE transactions on pattern analysis and machine intelligence》2007,29(10):1802-1817
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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 相似文献
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《Neural Networks, IEEE Transactions on》2008,19(12):2088-2098
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Polyhedral object recognition by indexing 总被引:1,自引:0,他引:1
In computer vision, the indexing problem is the problem of recognizing a few objects in a large database of objects while avoiding the help of the classical image-feature-to-object-feature matching paradigm. In this paper we address the problem of recognizing three-dimensional (3-D) polyhedral objects from 2-D images by indexing. Both the objects to be recognized and the images are represented by weighted graphs. The indexing problem is therefore the problem of determining whether a graph extracted from the image is present or absent in a database of model graphs. We introduce a novel method for performing this graph indexing process which is based both on polynomial characterization of binary and weighted graphs and on hashing. We describe in detail this polynomial characterization and then we show how it can be used in the context of polyhedral object recognition. Next we describe a practical recognition-by-indexing system that includes the organization of the database, the representation of polyhedral objects in terms of 2-D characteristic views, the representation of this views in terms of weighted graphs and the associated image processing. Finally, some experimental results allow the evaluation of the system performance. 相似文献
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Matching 3-D Models to 2-D Images 总被引:2,自引:1,他引:1
We consider the problem of analytically characterizing the set of all 2-D images that a group of 3-D features may produce, and demonstrate that this is a useful thing to do. Our results apply for simple point features and point features with associated orientation vectors when we model projection as a 3-D to 2-D affine transformation. We show how to represent the set of images that a group of 3-D points produces with two lines (1-D subspaces), one in each of two orthogonal, high-dimensional spaces, where a single image group corresponds to one point in each space. The images of groups of oriented point features can be represented by a 2-D hyperbolic surface in a single high-dimensional space. The problem of matching an image to models is essentially reduced to the problem of matching a point to simple geometric structures. Moreover, we show that these are the simplest and lowest dimensional representations possible for these cases.We demonstrate the value of this way of approaching matching by applying our results to a variety of vision problems. In particular, we use this result to build a space-efficient indexing system that performs 3-D to 2-D matching by table lookup. This system is analytically built and accessed, accounts for the effects of sensing error, and is tested on real images. We also derive new results concerning the existence of invariants and non-accidental properties in this domain. Finally, we show that oriented points present unexpected difficulties: indexing requires fundamentally more space with oriented than with simple points, we must use more images in a motion sequence to determine the affine structure of oriented points, and the linear combinations result does not hold for oriented points. 相似文献
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Image database systems must effectively and efficiently handle and retrieve images from a large collection of images. A serious problem faced by these systems is the requirement to deal with the nonstationary database. In an image database system, image features are typically organized into an indexing structure, and updating the indexing structure involves many computations. In this paper, this difficult problem is converted into a constrained optimization problem, and the iteration-free clustering (IFC) algorithm based on the Lagrangian function, is presented for adapting the existing indexing structure for a nonstationary database. Experimental results concerning recall and precision indicate that the proposed method provides a binary tree that is almost optimal. Simulation results further demonstrate that the proposed algorithm can maintain 94% precision in seven-dimensional feature space, even when the number of new-coming images is one-half the number of images in the original database. Finally, our IFC algorithm outperforms other methods usually applied to image databases. 相似文献