Online composite shape recognition based on relevance feedback

This paper describes a novel method of online composite shape recognition interms of the relevance feedback technology to capture a user's intentions incrementally, and adynamic user modeling method to adapt to various users' styles. First, the relevance feedback isadapted to refine the recognition results and reduce the ambiguity incrementally based on theestablishment of a feature-based vector model of a user's sketches. Secondly, a dynamic usermodeling is introduced to model the user's sketching habits based on recording and analyzinghistorical information incrementally. A model-based matching strategy is also employed in the methodto recognize sketches dynamically. Experiments prove that the proposed method is both effective andefficient.

Image Semantic Automatic Annotation by Relevance Feedback

A large semantic gap exists between content based index retrieval(CBIR) and high-level semantic,additional semantic information should be attached to the images,it refers in three respects including semantic representation model,semantic information building and semantic retrieval techniques.In this paper,we introduce an associated semantic network and an automatic semantic annotation system.In the system,a semantic network model is employed as the semantic representation model,it uses semantic Key words,linguistic ontology and low-level features in semantic similarity calculating.Through several times of users' relevance feedback,semantic network is enriched automatically.To speed up the growth of semantic network and get a balance annotation,semantic seeds and semantic loners are employed especially.

An Improved Asymmetric Bagging Relevance Feedback Strategy for Medical Image Retrieval

Much attention has been paid to relevant feedback in intelligent computation for social computing, especially in content-based image retrieval which based on WeChat platform for the medical auxiliary. It has a good effect on reducing the semantic gap between high semantics and low semantics of images. There are many kinds of support vector machines (SVM) based relevance feedback methods in image retrieval, but all of them may encounter some problems, such as a small size of sample, an asymmetric positive sample and negative sample as well as a long feedback cycle. To deal with these problems, an improved asymmetric bagging (IAB) relevance feedback algorithm is proposed. Furthermore, we apply a new fuzzy support machine (FSVM) to cooperate with IAB. To solve the over-fitting and real-time problems, we use modified local binary patterns (MLBP) as image features. Finally, experimental results demonstrate that our method performs other methods in terms of improving retrieval precision as well as retrieval efficiency.

Online Metric Learning for Relevance Feedback in E-Commerce Image Retrieval Applications

Relevance feedback plays a key role in multiple feature-based image retrieval applications. This paper describes an online metric learning approach for a set of ranking functions. In the feedback round, the most relevant and most nonrelevant images related to the target image are selected to construct a relative comparison triplet. The weighting parameters of the multiple ranking functions are updated by minimizing a quadratic objective function constrained by the triplet. The approach unifies the learning algorithm for the most commonly used ranking functions. Thus, multiple features with their own ranking function can easily be employed in the ranking module without feature reconstruction. The method is computationally inexpensive and appropriate for large-scale e-commerce image retrieval applications. Customized ranking functions are well supported. Practically, simplified ranking functions yield better results when the number of query rounds is relatively small. Experiments with an image dataset from a real e-commerce platform show the superiority of the proposed approach.

Improving Retrieval Performance by Region Constraints and Relevance Feedback

In this paper, region features and relevance feedback are used to improve the performance of CBIR. Unlike existing region-based approaches where either individual regions are used or only simple spatial layout is modeled, the proposed approach simultaneously models both region properties and their spatial relationships in a probabilistic framework. Furthermore, the retrieval performance is improved by an adaptive filter based relevance feedback. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.

An improved SVM model for relevance feedback in remote sensing image retrieval

With the rapid development of satellite remote sensing technology and an ever-increasing number of Earth observation satellites being launched,the global volume of remotely sensed imagery has been growing exponentially.Processing the variety of remotely sensed data has increasingly been complex and difficult.It is also hard to efficiently and intelligently retrieve what users need from a massive database of images.This paper introduces an improved support vector machine(SVM)model,which optimizes the model parameters and selects the feature subset based on the particle swarm optimization(PSO)method and genetic algorithm(GA)for remote sensing image retrieval.The results from an image retrieval experiment show that our method outperforms traditional methods such as GRID,PSO,and GA in terms of consistency and stability.

Mass detection algorithm based on support vector machine and relevance feedback

To improve the detection of mass with appearance that borders on the similarity between mass and density tissues in the breast,an support vector machine classifier based on typical features is designed to classify the region of interest(ROI).Furthermore,relevance feedback is introduced to improve the performance of support vector machines.A new mass detection scheme based on the support vector machine and the relevance feedback is proposed.Simulation experiments on mammograms illustrate that the novel support vector machine classifier based on typical features can improve the detection performance of the featureless classifier by 5%,while the introduction of relevance feedback can further improve the detection performance to about 90%.

山东省高校临床实习护生工作准备度的潜在剖面分析及其影响因素

目的:探讨山东省高校实习护生工作准备度现状,分析不同类别护生工作准备度的潜在特征差异,并分析其影响因素,为制定针对性的工作准备度干预方案提供参考。方法:2023年9月—10月,采用一般人口学资料调查表、工作准备度量表、反馈寻求行为量表对山东省不同地区1 478名实习护生进行调查,并对工作准备度进行潜在类别分析。结果:1 478名实习护生工作准备度可分为低准备度组(26.05%)、中准备度组(41.88%)、高准备度组(32.07%)3个类别。与高准备度组比较,女性、反馈寻求行为越低,越容易进入高准备度组;与高准备度组比较,女性、本科学历、反馈寻求行为越低,越容易进入中准备度组;与中准备度组相比,寻求反馈行为越少越容易进入低准备度组。结论:山东省实习护生工作准备度水平可分为3类,护理管理者可根据不同类型实习护生的临床特征,提高实习护生反馈寻求行为,从而提升实习护生的工作准备度。

双重绩效反馈相关性对企业异质性创新战略抉择的影响研究

绩效反馈对组织创新的影响一直备受关注,然而历史绩效和行业绩效连续时期的反馈相关性对企业异质性创新战略抉择的影响机理尚未厘清。基于绩效反馈理论,以2010—2022年中国深沪A股高科技制造业上市公司为样本,探讨双重绩效反馈相关性对企业异质性创新战略抉择的影响机理,以及高管股权激励的调节效应。研究结果显示,双重绩效反馈相关性与企业突破性创新呈U型关系,双重绩效反馈相关性与企业渐进性创新呈倒U型关系。高管股权激励强化了双重绩效反馈强相关与企业突破性创新之间的关系,也强化了弱相关与企业渐进性创新之间的关系。

新入职护士基本心理需要的潜在分型及对反馈寻求行为的影响

目的探讨新入职护士基本心理需要满足的潜在类别及与反馈寻求行为的关系。方法采用分层抽样法和便利抽样法于2022年10-12月使用一般资料调查表、基本心理需要满足量表、反馈寻求行为量表选取8个省份12家三级甲等医院415名新入职护士进行调查,并针对基本心理需要满足的结果进行潜在剖面分析。结果新入职护士基本心理需要满足可分为需要满足称心型(23.7%)、需要满足中等型(44.7%)和需要满足受挫型(31.6%)。基本心理需要满足潜在类别对反馈寻求行为的影响显著,称心型和中等型新入职护士的反馈寻求行为均高于受挫型(均P<0.05)。结论新入职护士基本心理需要满足存在异质性。护理管理者应关注基本心理需要满足为受挫型的新入职护士,可根据不同潜在剖面进行个体化心理干预以增强新入职护士基本心理需要的满足,并提高其反馈寻求水平。

Study and application of crown feedback control in hot strip rolling

Crown feedback control is one part of the automatic shape control （ASC） system. On the basis of large simulation researches conducted, a linear crown feedback control model was put forward and applied in actual strip rolling. According to its successful op- eration in the ASP 1700 hot strip mill of Angang Group for one year and also from the statistical results of several crown measurements, it can be definitely said that this control model is highly effective and shows stable performance. The control effectiveness of different gauges of strips with the feedback control is found to increase by 10%-30% compared with that without feedback control.

Ranking of Web Pages in a Personalized Search

The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile;this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that;the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.

面向稠密检索的伪相关反馈方法

伪相关反馈(PRF)机制是一种自动化的查询扩展(QE)技术,它利用原始查询和初次检索中前N篇文档蕴含的信息构建更加准确的查询,从而进一步提高信息检索系统的性能。但是,现有的面向稠密检索的PRF方法由于对文本的截断处理容易造成语义信息的缺失,而且在检索阶段的空间复杂度较高。针对上述问题,提出了一种基于段落级粒度且适用于长文本稠密检索的PRF方法 Dense-PRF。首先,通过计算语义距离从初次检索的前N篇文档中获得相关段落的向量;其次,对相关段落向量进行平均池化以得到QE项向量;然后,按照权重结合原始查询向量和QE项向量构建新的查询向量;最后,根据新的查询向量得到最终检索结果。在Robust04和WT2G两个经典长文本测试集上将Dense-PRF与基线模型进行了对比实验,相较于模型RepBERT+BM25,Dense-PRF在前20篇文档的准确率和归一化折现累计效益(NDCG)指标上分别提升了1.66、1.32个百分点和2.30、1.91个百分点。实验结果表明Dense-PRF能有效缓解查询与文档词汇不匹配的问题,并提升检索精度。

护士职业生涯韧性潜在剖面分析

目的探究护士职业生涯韧性潜在类别并分析影响因素。方法采用一般资料调查表、护士职业生涯韧性量表、角色宽度自我效能测量量表、主动性人格量表和反馈寻求行为量表对山东省两所三级甲等医院492名护士进行调查,采用潜在剖面分析和有序多分类Logistic回归探究职业生涯韧性潜在类别并分析其影响因素。结果护士职业生涯韧性可分为3个潜在类别:高水平组(5.69%)、中水平组(33.33%)和低水平组(60.98%)。年龄、工作年限、聘用方式、角色宽度自我效能、主动性人格和反馈寻求行为是护士职业生涯韧性潜在类别的影响因素(P<0.01)。结论护士职业生涯韧性分类特征明显,护理管理者应结合影响因素实施针对性干预,提高其职业生涯韧性水平。

多目标应急物流双向配送的关联反馈模型仿真

为了能够及时将物品输送至目的地,尽量避免紧急事件带来的负面影响,提出基于关联反馈的多目标应急物流双向配送方法。分析受灾区域路段的可通行性,考虑多影响因素下的应急物流配送路段相对长度,基于ANP理论构建关联反馈结构模型,并利用模型对应急物流双向配送的难易程度展开分析,计算多影响因素的权重系数,基于计算结果获取应急物流配送路径当量长度;再以此建立应急物流配送模型,通过上述模型制定合理的配送方案,从而实现多目标应急物流双向配送。实验结果表明,通过对所提方法开展配送总线路长度对比测试、平均耗时对比测试,验证了上述方法的有效性。

基于ConceptNet语义的伪相关反馈信息检索方法

伪相关性反馈技术在信息检索领域应用广泛,在考虑词频和逆文档频率等重要特征时,传统的信息检索方法容易忽略查询词本身的语义信息。提出了一种基于语义的伪相关性反馈信息检索方法SPRF(Semantic Pseudo-Relevance Feedback),充分利用ConceptNet获取语义信息,不仅考虑了查询词在文档中的词频重要性,还将查询词的语义信息整合到伪相关反馈框架中,以改善查询扩展词的选择。在6个TREC数据集上实验结果表明:SPRF方法对比较强基线模型和几种基于神经网络的方法在P@10和MAP两个指标上具有显著提升。

iFind:一个结合语义和视觉特征的图像相关反馈检索系统

给出了一个结合语义与视觉特征信息的图像相关反馈检索系统—— i Find.系统通过图像的标注信息构造语义网络 ,并在相关反馈中与图像的视觉特征相结合 ,有效地实现了在两个层次上的相关反馈 ,在基于内容的图像检索中取得了较为理想的效果 ,具有一定的应用价值 .

基于伪相关反馈的跨语言查询扩展

相关反馈是一种重要的查询重构技术，本文分析了两类相关反馈技术，一是按用户是否参与可分为伪相关反馈和交互式相关反馈，二是按作用于查询的方式可分为查询扩展与检索词重新加权。在此基础上，本文重点探讨了将相关反馈技术应用于跨语言信息检索，提出了翻译前查询扩展、翻译后查询扩展、翻译前与翻译后相结合的查询扩展三种方法。最后，本文通过伪相关反馈实验对这三种方法进行了比较，实验结果显示，三种跨语言查询扩展方法都能够有效地提高检索结果的精度，其中翻译后查询扩展方法相对更优越。此外，查询式的长度对不同跨语言查询扩展方法产生着不同程度的影响。

基于内容图像检索中相关反馈技术的回顾

由于相关反馈技术能有效地提高基于内容图像检索的性能,使它成为图像检索系统中不可少的一部分.近年来相关反馈技术的研究正吸引着越来越多的关注,涌现出了许多算法.在简要介绍了基于内容图像检索后,文中讨论了相关反馈的交互过程和其中的重要环节,进一步分析了相关反馈中的学习问题及其特点,根据相关反馈算法所采用的检索模型把算法分为基于距离度量的方法、基于概率框架的方法和基于机器学习的方法,并在这个分类下对近年来有代表性的一些算法进行了分析和探讨,最后展望了相关反馈技术未来的发展方向.

基于粒子群的图像检索相关反馈算法

将粒子群优化算法的进化搜索过程与用户的反馈过程有效结合,提出了一种基于粒子群的图像检索相关反馈算法,避免了初始检索对用户认知的影响以及对反馈效果造成的局限性,并使得用户对检索目标的理解逐渐清晰,能够有效全面的搜索图片库,同时避免多次反馈造成的算法效率和检索效果之间的矛盾.通过实验验证了算法的有效性.