Modelling of chromatic contrast for retrieval of wallpaper images

Colour remains one of the key factors in presenting an object and, consequently, has been widely applied in retrieval of images based on their visual contents. However, a colour appearance changes with the change of viewing surroundings, the phenomenon that has not been paid attention yet while performing colour‐based image retrieval. To comprehend this effect, in this article, a chromatic contrast model, CAMcc, is developed for the application of retrieval of colour intensive images, cementing the gap that most of existing colour models lack to fill by taking simultaneous colour contrast into account. Subsequently, the model is applied to the retrieval task on a collection of museum wallpapers of colour‐rich images. In comparison with current popular colour models including CIECAM02, HSI and RGB, with respect to both foreground and background colours, CAMcc appears to outperform the others with retrieved results being closer to query images. In addition, CAMcc focuses more on foreground colours, especially by maintaining the balance between both foreground and background colours, while the rest of existing models take on dominant colours that are perceived the most, usually background tones. Significantly, the contribution of the investigation lies in not only the improvement of the accuracy of colour‐based image retrieval but also the development of colour contrast model that warrants an important place in colour and computer vision theory, leading to deciphering the insight of this age‐old topic of chromatic contrast in colour science. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 361–373, 2015     

基于多尺度双线性卷积神经网络的多角度下车型精细识别

针对多角度下车辆出现一定的尺度变化和形变导致很难被准确识别的问题，提出基于多尺度双线性卷积神经网络（MS-B-CNN）的车型精细识别模型。首先，对双线性卷积神经网络（B-CNN）算法进行改进，提出MS-B-CNN算法对不同卷积层的特征进行了多尺度融合，以提高特征表达能力；此外，还采用基于中心损失函数与Softmax损失函数联合学习的策略，在Softmax损失函数基础上分别对训练集每个类别在特征空间维护一个类中心，在训练过程中新增加样本时，网络会约束样本的分类中心距离，以提高多角度情况下的车型识别的能力。实验结果显示，该车型识别模型在CompCars数据集上的正确率达到了93.63%，验证了模型在多角度情况下的准确性和鲁棒性。     

Empirical comparison of image retrieval color similarity methods with human judgment

It is important to verify assumptions and methods of image retrieval against actual human behavior. A study was conducted to compare similarity methods of color histograms against human assessment of similarity. The similarity methods tested include basic histogram intersection, center histogram matching, locality histogram matching, and size-weighted histogram matching. 161 subjects participated in the empirical study. The findings, based on Spearman correlation analysis, showed that both the basic histogram intersection method and size-weighted histogram are very close to human assessment of similarity (Spearman correlation coefficient of 0.915). The other two are not close to human judgment on similarity. This study illustrates an alternative approach to evaluating matching algorithms. Unlike the usual measures of recall and precision, this approach emphasizes human validation. Fewer images are required with the use of statistical testing.     

Using Web resources to construct multilingual medical thesaurus for cross-language medical information retrieval

Due to the language barrier, non-English users are unable to retrieve the most updated medical information from the U.S. authoritative medical websites, such as PubMed and MedlinePlus. However, currently, there is no any cross-language medical information retrieval (CLMIR) system that can help Chinese-speaking consumers cross the language barrier in finding useful English medical information. A few CLMIR systems utilize MeSH (Medical Subject Headings) to help overcome the language barrier. Unfortunately, the traditional Chinese version of MeSH is currently unavailable.In this paper, we employ a semi-automatic term translation method to construct a Chinese–English MeSH by exploiting abundant multilingual Web resources, including Web anchor texts and search–result pages. Through this method, we have developed a Chinese–English Mesh Compilation System to assist knowledge engineers in compiling a Chinese–English medical thesaurus with more than 19,000 entries. Furthermore, this thesaurus has been used to develop a prototypical system for cross-language medical information retrieval, MMODE, which can help consumers retrieve top-quality English medical information using Chinese terms.     

Differential effects of cue changes and task changes on task-set selection costs.

A task-switching paradigm with a 2:1 mapping between cues and tasks was used to separate cue-switching processes (indexed through pure cue-switch costs) from actual task-switching processes (indexed through additional costs in case of cue and task changes). A large portion of total switch costs was due to cue changes (Experiments 1 and 2), and cue-switch costs but not task-switch costs were sensitive to effects of practice (Experiment 1) and preparation (Experiment 2). In contrast, task-switch costs were particularly sensitive to response-priming effects (Experiments 1 and 2) and task-set inhibition (Experiment 3). Results suggest two processing stages relevant during task-set selection: cue-driven retrieval of task rules from long-term memory and the automatic application of rules to a particular stimulus situation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Similarity retrieval of iconic image database

The perception of spatial relationships among objects in a picture is one of the important selection criteria to discriminate and retrieve the images in an iconic image database system. The data structure called 2D string, proposed by Chang et al., is adopted to represent symbolic pictures. The 2D string preserves the objects' spatial knowledge embedded in images. Since spatial relationship is a fuzzy concept, the capability of similarity retrieval for the retrieval by subpicture is essential. In this paper, similarity measure based on 2D string longest common subsequence is defined. The algorithm for similarity retrieval is also proposed. Similarity retrieval provides the iconic image database with the distinguishing function different from a conventional database.     

基于奇异值分解和神经网络的个性化信息模式识别

随着信息资源的持续增长，信息获取系统为特定用户提供特定信息服务的需求越来越重要。该文提出了基于奇异值分解和神经网络的个性化信息模式识别的方法，可以方便地获取用户信息需求的特征，并且利用这些特征信息完成个性化的信息识别和匹配，即实现个性化信息的模式识别，进而实现信息的个性化获取和管理。该文中的算法实现简单，可以方便地应用到目前的一些信息管理、服务系统中。