共查询到20条相似文献，搜索用时 32 毫秒
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AbstractThis column discusses the point-of-care tool Clinical Pharmacology. This review is primarily intended for newer health sciences librarians who are learning about drug references and clinical decision-making support systems or health sciences librarians making collection development decisions, although any librarian will find this review useful. A sample search will be provided to highlight the database’s unique features as well as a comparison to other resources. 相似文献
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探讨在图像检索中,当用户以感觉、印象、情绪等情感特征作为相关性评价标准筛选结果时,缩略图尺寸对用户评价与选择结果的影响。根据评价实验发现:缩略图尺寸会在一定程度上影响用户对一组图片的情感评价与选择结果;其影响程度大小与检索任务类型以及同组内图片视觉相似程度和复杂度有关。图片越大,用户评价与选择的结果越稳定;同组图片的视觉差异越小、图片内容越复杂,用户评价与选择的结果越不稳定。 相似文献
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Karen Crowell 《Medical reference services quarterly》2013,32(2):215-223
ClinicalAccess is a new clinical decision support tool that uses a question-and-answer format to mirror clinical decision-making strategies. The unique format of ClinicalAccess delivers concise, authoritative answers to more than 120,000 clinical questions. This column presents a review of the product, a sample search, and a comparison with other point-of-care search engines. 相似文献
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［目的/意义］ 信息技术的快速发展与广泛应用推动了敦煌学研究的变革,使敦煌学研究及其知识资源的利用更加便利,由于还停留在现有数据库传统知识平台与检索方式上,使敦煌遗书图像知识的价值挖掘不全面。为此,笔者对敦煌遗书图像研究进行梳理,对敦煌图像知识发现的深度和广度及其知识关联进行充分的语义描述,以利于发掘敦煌遗书图像的多元价值。［方法/过程］ 通过敦煌遗书图像研究现状的考量、语义描述,根据敦煌遗书图像语义特征设计其语义特征层级模型,结合智能+关联数据技术构建敦煌遗书图像知识关联的组织框架,并深入分析了敦煌遗书图像知识关联及其组织框架中的数据收集层、语义描述层、数据关联层、资源应用层。［结果/结论］ 提出敦煌遗书知识关联模型的实践价值,能够打通与外部开放数据关联渠道、提高敦煌遗书图像智能化的检索质量以及提升敦煌遗书图像知识服务的深度与广度。 相似文献
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ABSTRACT As an important part of art and culture, ancient murals depict a variety of different artistic images, and these individual images have important research value. For research purposes, it is often important to first determine the type of objects represented in a painting. However, the mural painting environment makes datasets difficult to collect, and long-term exposure leads to underlying features that are not distinct, which makes this task challenging. This study proposes a convolutional neural network model based on the classic AlexNet network model and combines it with feature fusion to automatically classify ancient mural images. Due to the lack of large-scale mural datasets, the model first expands the dataset by applying image enhancement algorithms such as scaling, brightness conversion, noise addition, and flipping; then, it extracts the underlying features (such as fresco edges) shared by the first stage of a dual channel structure. Subsequently, a second-stage deep abstraction is conducted on the features extracted by the first stage using a two-channel network, each of which has a different structure. The obtained characteristics from both channels are merged, and a loss function is constructed to obtain the classification result. This approach improves the model's robustness and feature expression ability. The model achieves an accuracy of 84.24%, a recall rate of 84.15%, and an F1-measure of 84.13% when applied to a constructed mural image dataset. Compared with the AlexNet model and other improved convolutional neural network models, the proposed model improves each evaluation index by approximately 5%, verifying the rationality and effectiveness of the model for automatic mural image classification. The mural classification model proposed in this paper comprehensively considers the influences of network width and depth and can extract rich details from mural images from multiple local channels. An effective classification method could help researchers manage and protect mural images in an orderly fashion and quickly and effectively search for target images in a digital mural library based on a specified image category, aiding mural condition monitoring and restoration efforts as well as archaeological and art historical research. 相似文献
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提出一种基于组合特征的在线商标图像检索系统设计方法,该方法利用颜色直方图匹配、二维傅立叶形状描述子,及二维傅立叶变换形状描述方法与Zemike矩相结合的组合形状描述子,从粗到细、多层次提取商标图像形状特征,用欧氏距离度量形状相似性。用标准MPEG-7的CE2B图像库和1000多张商标图像库进行检索实验,并与现有方法进行比较,表现出其较好的检索性能。 相似文献
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[目的/意义]以用户情感为线索的图像检索已成为机器学习研究的热点，但图像情感特征标注的语料数据多来源于对图像低层特征的抽取，从而导致图像检索过程单一化和程式化。本文提出了一种基于深度学习的图像情感特征抽取的算法，将图像底层特征融合到图像的高层情感语义当中，为实现图像的情感语义检索提供了参考。[方法/过程]利用改进的卷积网络模型，将数据集图像的颜色、纹理作为输入，经多层运算自动提取图像的情感信息，并通过反向传播算法计算出改进后模型的情感检索准确率，构造出准确率较高且过拟合程度低的图像情感特征提取模型。[结果/结论]应用改进的卷积神经网络模型，实现了对图像情感特征的抽取，相较于原模型提升了10%的检索准确率。 相似文献
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面向数字人文的京剧脸谱图像数字资源构建 总被引：2，自引：0，他引：2
京剧脸谱的"非物质"特征决定了脸谱图像的数字化保护应该更重视"活态"的传承,尤其是需要从程式化、象征性和装饰性特征出发来认识京剧脸谱图像资源。针对京剧脸谱图像保护、传承和发展中面临的困境,提出面向数字人文的京剧脸谱图像数字资源建设路径,通过结合数字资源呈现展现京剧脸谱图像数字资源建设的成果。利用"北京记忆-京剧脸谱"网站建设案例展现面向数字人文的京剧脸谱图像数字资源构建的可行性。 相似文献
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面向科技文献的多模态语义关联特征提取与表达体系研究 总被引：1，自引：0，他引：1
科技文献资源是一种多模态数据,除文本信息外,还包含丰富的图像、表格、公式、音频、视频等多种模态的信息,有利于用户充分理解科技文献资源中的知识。该文把多模态思想引入科技文献的语义表示方面,对科技文献中的图像、表格和公式信息进行语义分析,与文本信息共同表示文献语义内容,通过科技文献中多种模态信息的语义表示及相互关系完善科技文献内容的语义化表示,发展刻画科技文献对象多态性的表达体系。 相似文献
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敦煌遗书图像蕴含丰富的文化内涵,对于研究中国古代社会历史、宗教与美术具有重要意义,但传统单一线性的图像检索方式不利于敦煌遗书图像隐性知识的挖掘,影响知识发现的深度与广度。而关联数据能够连接多源异构资源,实现多种资源的语义互联,既能促进管理标准化与规范化,又有利于提升图像内容的深入整合,同时,将关联数据应用于敦煌遗书图像在理论、实践与技术上都具备可行性。为此,本文针对敦煌遗书图像的物理特征与内容语义特征构建敦煌遗书图像层次模型,使用元数据描述后将这些元数据进行关联;同时,本文基于关联数据设计敦煌遗书图像知识关联的组织模式,其自底向上分为数据收集层、语义描述层、数据关联层与知识应用层四层,旨在改善图像检索效果并利于敦煌遗书图像的知识发现与智能查询。 相似文献
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[目的/意义] 提出利用社会标签自动分类图片情感类型的方法,服务基于情感特征的图像检索与利用。[方法/过程] 以Flickr图片为例,利用PMI算法对WordNet-Affect词表进行预处理形成典型情感词表;结合Ekman提出的6类基本情感类型,利用标签对图片情感类型进行标注;并且,通过实验对分类标注效果进行验证;最后,讨论图片特点、标注意图、非情感标签数量对分类标注效果的影响。[结果/结论] 研究发现,一幅图片的非情感标签与情感标签在表现图片整体情感类型的倾向性上具有较高一致性;结合PMI算法,利用预处理后的典型情感词表标注图片的结果优于未处理的WordNet-Affect词表;并且,分类标注效果与人工标注结果也具有较好的一致性,其中,快乐类（Happy）和忧伤类（Sad）图片的分类标注一致性最高,惊讶类（Surprise）的分类标注一致性最低;分析发现,仅通过标签标注图片情感类型的过程中,分类标注效果与图片情感的典型性、单一性以及图片发布方和欣赏者意图、动机的差异、图片的非情感标签个数都有关系。 相似文献
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《Journal of Informetrics》2022,16(4):101333
The number of clinical citations received from clinical guidelines or clinical trials has been considered as one of the most appropriate indicators for quantifying the clinical impact of biomedical papers. Therefore, the early prediction of clinical citation count of biomedical papers is critical to scientific activities in biomedicine, such as research evaluation, resource allocation, and clinical translation. In this study, we designed a four-layer multilayer perceptron neural network (MPNN) model to predict the clinical citation count of biomedical papers in the future by using 9,822,620 biomedical papers published from 1985 to 2005. We extracted ninety-one paper features from three dimensions as the input of the model, including twenty-one features in the paper dimension, thirty-five in the reference dimension, and thirty-five in the citing paper dimension. In each dimension, the features can be classified into three categories, i.e., the citation-related features, the clinical translation-related features, and the topic-related features. Besides, in the paper dimension, we also considered the features that have previously been demonstrated to be related to the citation counts of research papers. The results showed that the proposed MPNN model outperformed the other five baseline models, and the features in the reference dimension were the most important. In all the three dimensions, the citation-related and topic-related features were more important than the clinical translation-related features for the prediction. It also turned out that the features helpful in predicting the citation count of papers are not important for predicting the clinical citation count of biomedical papers. Furthermore, we explored the MPNN model based on different categories of biomedical papers. The results showed that the clinical translation-related features were more important for the prediction of clinical citation count of basic papers rather than those papers closer to clinical science. This study provided a novel dimension (i.e., the reference dimension) for the research community and could be applied to other related research tasks, such as the research assessment for translational programs. In addition, the findings in this study could be useful for biomedical authors (especially for those in basic science) to get more attention from clinical research. 相似文献
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《Journal of Web Librarianship》2013,7(2):196-213
Information professionals and librarians have been studying, discussing, and developing digital libraries for more than two decades, but understanding ultimate use of images from digital libraries remains a mystery for many of them. Most articles written on digital library use focus on users’ search retrieval needs and behavior. Few mention how digital library patrons use the images they request. Like many digital libraries, archives, and special collections, the University of Houston Digital Library makes high resolution images available to its patrons. Image delivery is achieved by an automated system, titled the Digital Cart Service. An unexpected benefit of the Digital Cart Service is the reporting mechanism that produces data that includes intended use information. This article discusses the analysis of this data to determine why images were used, what products were created from the images, and what implications this has on digital library management. The authors believe that answering these questions creates an environment in which digital library innovators can better promote and design digital libraries, and describe and select the content in them. 相似文献
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Multimodal biomedical image indexing and retrieval using descriptive text and global feature mapping
Matthew S. Simpson Dina Demner-Fushman Sameer K. Antani George R. Thoma 《Information Retrieval》2014,17(3):229-264
The images found within biomedical articles are sources of essential information useful for a variety of tasks. Due to the rapid growth of biomedical knowledge, image retrieval systems are increasingly becoming necessary tools for quickly accessing the most relevant images from the literature for a given information need. Unfortunately, article text can be a poor substitute for image content, limiting the effectiveness of existing text-based retrieval methods. Additionally, the use of visual similarity by content-based retrieval methods as the sole indicator of image relevance is problematic since the importance of an image can depend on its context rather than its appearance. For biomedical image retrieval, multimodal approaches are often desirable. We describe in this work a practical multimodal solution for indexing and retrieving the images contained in biomedical articles. Recognizing the importance of text in determining image relevance, our method combines a predominately text-based image representation with a limited amount of visual information, in the form of quantized content-based visual features, through a process called global feature mapping. The resulting multimodal image surrogates are easily indexed and searched using existing text-based retrieval systems. Our experimental results demonstrate that our multimodal strategy significantly improves upon the retrieval accuracy of existing approaches. In addition, unlike many retrieval methods that utilize content-based visual features, the response time of our approach is negligible, making it suitable for use with large collections. 相似文献
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数字图像的语义描述与标注是解决图像检索中语义鸿沟问题的关键。由于缺乏面向领域的有效的数字图像描述方法规范,基于图像底层视觉特征的机器标注和基于专家知识的人为标注的标注结果都存在标注信息质量不高和结果不统一的问题。针对这一现实问题,本文基于图像元数据和信息需求理论,针对敦煌壁画数字图像这一特定文化遗产领域,提出了语义描述框架和领域主题词表相结合的数字图像内容语义描述方法,详细阐述了语义层次及其相互关系。同时,从图像语义描述粒度的角度讨论了语义粒度大小对标注成本的影响,以及该语义描述框架的可移植性问题。图4。表5。参考文献25。 相似文献
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简要介绍了图书馆联盟及其主要功能，分析了图书馆联盟的技术特征，提出了基于多智能体的图书馆联盟建构方案，分析了各智能体的功能及基本结构。 相似文献
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Kate Drazner Hoyt 《Critical Studies in Media Communication》2019,36(2):103-121
In the years since Michael Brown’s death, the hashtag #HandsUpDontShoot has been criticized for supposedly misrepresenting forensic evidence as framed by the Department of Justice. However, an expressive pull has kept alive both the hashtag and the sentiment behind it. The images of #HandsUpDontShoot are compelling in that they offer a glimpse into lived experiences that are often dismissed, ignored, or refuted. In this essay, I trace the aesthetic features of the #HandsUpDontShoot images, which foreground shocking juxtapositions between nonviolent protesters and militarized police forces, to the hashtag’s historical analogue: antilynching photography. Antilynching photography often utilized the aesthetic techniques of remediation, recontextualization, and juxtaposition—aesthetic features used prominently in today’s digital and remix cultures. By noting #HandsUpDontShoot’s use of these same techniques, I illuminate the ways in which Twitter’s connective affordances shape the viewer’s encounter with the images to engender ethical witnessing by affectively linking Brown’s death to shared material experiences of racial minorities. Such encounters propel witnesses beyond distanced objectification and toward an embodied reckoning of those experiences. 相似文献
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图像索引与检索的数据库方法 总被引：3，自引：0，他引：3
图像资源的迅速增长使我们面临新的挑战, 迫使人们对其索引与检索技术进行深入研究。本文讨论了图像索引的数据库方法,具体论述了图像的颜色、纹理、形状基本特征的抽取和对分类、主题、标题、创建者等外部特征与内容特征的描述，建立索引支持快速检索。. 相似文献