一种基于相关反馈的图像内在语义模型

本文介绍了一种新的语义模型-图像内在语义模型ⅡSM（image internal semantic model）。这种模型通过分析相关反馈的图像检索的结果来提取语义信息，然后对所有图像计算两两之间的互信息，利用互信息对图像进行语义聚类。实验结果表明这种语义模型利用图像之间的语义相关性能提高查询的效率，并且能较准确的将数据库中的图像按语义分类。     

A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel function

Relevance feedback (RF) is an effective approach to bridge the gap between low-level visual features and high-level semantic meanings in content-based image retrieval (CBIR). The support vector machine (SVM) based RF mechanisms have been used in different fields of image retrieval, but they often treat all positive and negative feedback samples equally, which will inevitably degrade the effectiveness of SVM-based RF approaches for CBIR. In fact, positive and negative feedback samples, different positive feedback samples, and different negative feedback samples all always have distinct properties. Moreover, each feedback interaction process is usually tedious and time-consuming because of complex visual features, so if too many times of iteration of feedback are asked, users may be impatient to interact with the CBIR system. To overcome the above limitations, we propose a new SVM-based RF approach using probabilistic feature and weighted kernel function in this paper. Firstly, the probabilistic features of each image are extracted by using principal components analysis (PCA) and the adapted Gaussian mixture models (AGMM) based dimension reduction, and the similarity is computed by employing Kullback–Leibler divergence. Secondly, the positive feedback samples and negative feedback samples are marked, and all feedback samples’ weight values are computed by utilizing the samples-based Relief feature weighting. Finally, the SVM kernel function is modified dynamically according to the feedback samples’ weight values. Extensive simulations on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches.     

基于模糊测度的知识关联性建模方法

在教学应用场景中,知识之间的关联性广受关注,但现有研究通常偏重两两知识点之间关系的建模,忽视知识集合中复杂的关联关系,导致研究结果出现偏差.因此,文中引入模糊测度对知识集合进行量化度量,并在此基础上提出基于模糊测度的知识关联性建模方法.首先,基于认知心理学理论,分析知识间存在的三种不同关系,并利用模糊测度建模知识间的关联性,通过实际教学场景论证方法的实用性.然后,在模糊测度建模的基础上,从知识关联性的视角讨论知识的重要度和交互指标.最后,研究知识关联性在认知诊断中的应用.真实数据集上的实验证实知识关联性对认知诊断的影响,不仅有效提升预测精度,也提供更好的可解释性.     

Ruminations of an Editor-in-Chief

The Journal of Strategic Information Systems (JSIS) was first published December 1991 by Robert (Bob) D. Galliers, its Founding Editor-in-Chief (EiC). Early on Bob invited me to join the JSIS International Editorial Board¹ (the Board then being more operational, with members serving as quasi-AEs, an arrangement we’ve reinstituted more recently). I became Pacific Asia Region Editor in 2003, with JSIS shifting from region editors to Senior Editors in 2007, in which role I served the journal through the end of 2018. Mid-2018 Bob and Sirkka (Sirkka Jarvenpaa joined as co-EiC in 2000) jointly decided it was time; and announced they would both step down from their co-EiC roles at the end of that year. In September 2018, I was endorsed by Bob and Sirkka, the Senior Editors and Elsevier, to succeed Bob and Sirkka and assume the role of Editor-in-Chief of JSIS commencing 1 January 2019. I served as Editor-in-Chief through 1 July 2021 after which Yolande Chan and I served as co-Editors-in-Chief. Now the end of 2022, I am stepping down from operational involvement with the journal. In this my final editorial I focus on my time as EiC, also reflecting on my almost 3 decades with the journal. I much appreciate being invited to write these brief ruminations and am honoured to be included amongst the journal’s Emeritus, along with the esteemed Robert Galliers and Sirkka Jarvenpaa.     

基于预测值替代的相关向量回归稳健化方法

针对相关向量机的性能易受到奇异值影响的情况,提出了一种增强相关向量机稳健性的方法。其主要思想如下:首先用原始训练数据训练相关向量机;然后,利用某种准则,从原始数据中挑选一些样本,用其预测值代替输出变量值;随后,用改变后的训练样本重新训练相关向量机。这个过程可重复几次。数据试验表明,较之相关向量机和变分稳健相关向量机,新算法对奇异值更加不敏感。     

一种图像检索中的灰色相关反馈算法

在交互式CBIR系统中,由于用户的查询需求常常是模糊的,因此检索结果从某种意义上说是不确定的。于是,可以将图像检索过程视为一个“灰色系统”,其中的查询向量以及图像特征的权重可视为“灰数”。基于此,该文提出了一种新的相关反馈技术,它采用“灰关联分析”理论来分析和描述“例子图像”与“相关图像”之间的关系,据此自动更新查询向量与图像特征的权重,从而更准确地描述用户的查询需求。实验结果表明,这种相关反馈算法能较好地描述用户的查询需求,显著地改善了图像检索的性能。     

Active learning paradigms for CBIR systems based on optimum-path forest classification

This paper discusses methods for content-based image retrieval (CBIR) systems based on relevance feedback according to two active learning paradigms, named greedy and planned. In greedy methods, the system aims to return the most relevant images for a query at each iteration. In planned methods, the most informative images are returned during a few iterations and the most relevant ones are only presented afterward. In the past, we proposed a greedy approach based on optimum-path forest classification (OPF) and demonstrated its gain in effectiveness with respect to a planned method based on support-vector machines and another greedy approach based on multi-point query. In this work, we introduce a planned approach based on the OPF classifier and demonstrate its gain in effectiveness over all methods above using more image databases. In our tests, the most informative images are better obtained from images that are classified as relevant, which differs from the original definition. The results also indicate that both OPF-based methods require less user involvement (efficiency) to satisfy the user's expectation (effectiveness), and provide interactive response times.     

几种磁共振成像技术的相关性

本文从理论上对磁共振成像的三种最基本的成像技术进行研究,得到在一定条件下它们之间的相关性,这对磁共振成像的临床工作及其理论研究都有一定的参考价值。     

Project management scholarship: Relevance,impact and five integrative challenges for business and management schools

This paper discusses the standing of project management in the academy. It does so from the viewpoint of business and management schools. The paper identifies five critical integrative challenges concerning research, how they might be better addressed and perhaps turned into opportunities. The paper builds on recent debates within the area of engaged scholarship and knowledge co-production, which call for greater focus on multi-disciplinarity and research–practice collaborations. The paper offers suggestions as to what project management scholars could do to tackle the identified challenges and thereby improve the standing of project management as a subject area within the academy and its contribution to the curriculum and research agenda of business and management schools. The paper ends with some thoughts about future debates on the role of project management research and teaching, especially how project management scholarship could help respond to some of the current criticism of business school research and how research could better inform management practice.