共查询到20条相似文献，搜索用时 718 毫秒
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本文基于校园网构建动态图片管理网站,同时分析介绍了利用ASP和ACCESS构建图片显示动态网页的几种方法,以及分析了这几种思路在实现过程中所运用到的处理方法。 相似文献
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采用当前方法提取城市交通枢纽站区的空间特征时,选取的指标显著性系数较低,存在特征提取精准度低的问题。提出多维度视角下城市交通枢纽站区空间特征提取方法,通过交通拥堵时空累积指标分析并判别城市交通枢纽站区的交通运行状态,根据分析和判别结果构建城市交通枢纽站交通状态可视化模型,为城市交通枢纽站区的空间特征提取提供信息。在城市交通枢纽站交通状态可视化模型的基础上结合复杂网络理论,选取整体性指标和权值性指标,在参数特征维度、时间特征维度、空间特征维度和统计特征维度等多维度视角下利用度、度的分布、平均度、簇系数、平均路径长度、客流量点强度、平均运距点强度以及度和度的相关性指标得到城市交通枢纽站区的空间特征,在多维度视角下完成了城市交通枢纽站区的空间特征提取。实验结果表明,所提方法的特征提取精准度高。 相似文献
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运用生态学的相关理论与方法,从图书馆生态系统、图书馆生态群落、图书馆生态种群、图书馆生态个体等方面,分析了图书馆的生态位,并通过构建图书馆生态位评估指标、改善图书馆生态位质量、拓展新型生态位、加强生态位之间联系等角度,探讨了调控图书馆生态位的各种手段与方法. 相似文献
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大量图像信息的产生使得基于内容的图像检索技术成为研究热点.由于颜色特征具有稳定性和计算简单的特点,本文首先介绍了利用全局颜色直方图进行图像检索的基本思想,然后分析了它的局限性,并给出了改进的方法:特征提取采用结合空间信息的颜色一致向量方法.在特征度量时,依据所设计的评价实验,对这两种方法进行了比较,并给出了实验结果和图像检索性能的评价.实验表明,所述的图像检索方法具有较好的查全率和查准率. 相似文献
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《Information processing & management》2016,52(4):571-591
This work presents a content based semantics and image retrieval system for semantically categorized hierarchical image databases. Each module is designed with an aim to develop a system that works closer to human perception. Images are mapped to a multidimensional feature space, where images belonging a semantic are clustered and indexed to acquire its efficient representation. This helps in handling the existing variability or heterogeneity within this semantic. Adaptive combinations of the obtained depictions are utilized by the branch selection and pruning algorithms to identify some closer semantics and select only a part of the large hierarchical search space for actual search. So obtained search space is finally used to retrieve desired semantics and similar images corresponding to them. The system is evaluated in terms of accuracy of the retrieved semantics and precision-recall curves. Experiments show promising semantics and image retrieval results on hierarchical image databases. The results reported with non-hierarchical but categorized image databases further prove the efficacy of the proposed system. 相似文献
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《Information processing & management》2022,59(2):102868
With the continuous development of intelligent transportation systems, vehicle-related fields have emerged a research boom in detection, tracking, and retrieval. Vehicle re-identification aims to judge whether a specific vehicle appears in a video stream, which is a popular research direction. Previous researches have proven that the transformer is an efficient method in computer vision, which treats a visual image as a series of patch sequences. However, an efficient vehicle re-identification should consider the image feature and the attribute feature simultaneously. In this work, we propose a vehicle attribute transformer (VAT) for vehicle re-identification. First, we consider color and model as the most intuitive attributes of the vehicle, the vehicle color and model are relatively stable and easy to distinguish. Therefore, the color feature and the model feature are embedded in a transformer. Second, we consider that the shooting angle of each image may be different, so we encode the viewpoint of the vehicle image as another additional attribute. Besides, different attributes are supposed to have different importance. Based on this, we design a multi-attribute adaptive aggregation network, which can compare different attributes and assign different weights to the corresponding features. Finally, to optimize the proposed transformer network, we design a multi-sample dispersion triplet (MDT) loss. Not only the hardest samples based on hard mining strategy, but also some extra positive samples and negative samples are considered in this loss. The dispersion of multi-sample is utilized to dynamically adjust the loss, which can guide the network to learn more optimized division for feature space. Extensive experiments on popular vehicle re-identification datasets verify that the proposed method can achieve state-of-the-art performance. 相似文献
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The field of color image retrieval has been an important research area for several decades. For the purpose of effectively retrieving more similar images from the digital image databases, this paper uses the color distributions, the mean value and the standard deviation, to represent the global characteristics of the image. Moreover, the image bitmap is used to represent the local characteristics of the image for increasing the accuracy of the retrieval system. As the experimental results indicated, the proposed technique indeed outperforms other schemes in terms of retrieval accuracy and category retrieval ability. Furthermore, the total memory space for saving the image features of the proposed method is less than Chan and Liu’s method. 相似文献
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Traditional content based image retrieval attempts to retrieve images using syntactic features for a query image. Annotated image banks and Google allow the use of text to retrieve images. In this paper, we studied the task of using the content of an image to retrieve information in general. We describe the significance of object identification in an information retrieval paradigm that uses image set as intermediate means in indexing and matching. We also describe a unique Singapore Tourist Object Identification Collection with associated queries and relevance judgments for evaluating the new task and the need for efficient image matching using simple image features. We present comprehensive experimental evaluation on the effects of feature dimensions, context, spatial weightings, coverage of image indexes, and query devices on task performance. Lastly we describe the current system developed to support mobile image-based tourist information retrieval. 相似文献
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[目的/意义]图像包含了丰富、生动的信息,利用图像检索技术能够有效的对大规模图像信息进行分析、组织和处理,具有重要的实践意义。近年来,各国对图像检索的研究力度不断加大,有必要对国际图像检索文献进行梳理。[方法/过程]本文采用文献计量方法和CitespaceⅢ可视化工具,对收集Web of Science数据库中有关图像检索领域的文献进行分析,梳理了文献时间分布规律、学科分布状况,并重点从作者、机构和国家和关键词频的角度探索了国际图像检索领域的主要研究力量和研究热点。[结果/结论]通过总结和分析发现：图像检索领域的主要研究作者和机构大部分来自于中国;国际作者合作尚未形成较为规模的团队、美国与中国是图像检索领域研究的主要力量;目前的研究热点主要集中在基于内容的图像检索、图像分类和相关反馈这3个方面。 相似文献
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《Information processing & management》2023,60(1):103119
Multi-feature fusion has achieved gratifying performance in image retrieval. However, some existing fusion mechanisms would unfortunately make the result worse than expected due to the domain and visual diversity of images. As a result, a burning problem for applying feature fusion mechanism is how to figure out and improve the complementarity of multi-level heterogeneous features. To this end, this paper proposes an adaptive multi-feature fusion method via cross-entropy normalization for effective image retrieval. First, various low-level features (e.g., SIFT) and high-level semantic features based on deep learning are extracted. Under each level of feature representation, the initial similarity scores of the query image w.r.t. the target dataset are calculated. Second, we use an independent reference dataset to approximate the tail of the attained initial similarity score ranking curve by cross-entropy normalization. Then the area under the ranking curve is calculated as the indicator of the merit of corresponding feature (i.e., a smaller area indicates a more suitable feature.). Finally, fusion weights of each feature are assigned adaptively by the statistically elaborated areas. Extensive experiments on three public benchmark datasets have demonstrated that the proposed method can achieve superior performance compared with the existing methods, improving the metrics mAP by relatively 1.04% (for Holidays), 1.22% (for Oxf5k) and the N-S by relatively 0.04 (for UKbench), respectively. 相似文献
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分析了CBIR系统的结构模式,探讨了基于形状特征的图像检索系统构建问题。重点研究了形状描述、特征向量索引以及特征相似性度量及匹配等相关技术,应用轮廓点与兴趣点之间的空间分布关系构造形状描述函数并提取图像特征。实验结果表明,系统在动物形状测试集中具有较高的检索效率。 相似文献
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