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排序方式: 共有372条查询结果,搜索用时 15 毫秒
1.
以子块直方图彩色图像检索算法为基础, 分析了进一步利用图像空间相似信息的颜色匹配对检索算法的性能。在子块直方图的构成、直方图距离值的归类等方面提出了行之有效的改进方法;给出了子块大小、相似度阈值等参数选择的优化原则,使查准率、查全率等检索性能指标得到了较大的提高,得出了几个有用的结论并形成了实验系统。 相似文献
2.
An accurate and rapid method is required to retrieve the overwhelming majority of digital images. To date, image retrieval methods include content-based retrieval and keyword-based retrieval, the former utilizing visual features such as color and brightness, and the latter utilizing keywords that describe the image. However, the effectiveness of these methods in providing the exact images the user wants has been under scrutiny. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as feedback during the retrieval session in order to define a user’s need and provide an improved result. Methods that employ relevance feedback, however, do have drawbacks because several pieces of feedback are necessary to produce an appropriate result, and the feedback information cannot be reused. In this paper, a novel retrieval model is proposed, which annotates an image with keywords and modifies the confidence level of the keywords in response to the user’s feedback. In the proposed model, not only the images that have been given feedback, but also other images with visual features similar to the features used to distinguish the positive images are subjected to confidence modification. This allows for modification of a large number of images with relatively little feedback, ultimately leading to faster and more accurate retrieval results. An experiment was performed to verify the effectiveness of the proposed model, and the result demonstrated a rapid increase in recall and precision using the same amount of feedback. 相似文献
3.
Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be
interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal
branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under
the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes
the Infonorma requirements specification, the architectural design solution for those requirements, the detailed design of
the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology. 相似文献
4.
Yolanda Blanco-Fernndez Jos J. Pazos-Arias Alberto Gil-Solla Manuel Ramos-Cabrer Martín Lpez-Nores Jorge García-Duque Ana Fernndez-Vilas Rebeca P. Díaz-Redondo Jesús Bermejo-Muoz 《Knowledge》2008,21(4):305-320
Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV. 相似文献
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In Content-based Image Retrieval (CBIR), the user provides the query image in which only a selective portion of the image carries the foremost vital information known as the object region of the image. However, the human visual system also focuses on a particular salient region of an image to instinctively understand its semantic meaning. Therefore, the human visual attention technique can be well imposed in the CBIR scheme. Inspired by these facts, we initially utilized the signature saliency map-based approach to decompose the image into its respective main object region (ObR) and non-object region (NObR). ObR possesses most of the vital image information, so block-level normalized singular value decomposition (SVD) has been used to extract salient features of the ObR. In most natural images, NObR plays a significant role in understanding the actual semantic meaning of the image. Accordingly, multi-directional texture features have been extracted from NObR using Gabor filter on different wavelengths. Since the importance of ObR and NObR features are not equal, a new homogeneity-based similarity matching approach has been devised to enhance retrieval accuracy. Finally, we have demonstrated retrieval performances using both the combined and distinct ObR and NObR features on seven standard coral, texture, object, and heterogeneous datasets. The experimental outcomes show that the proposed CBIR system has a promising retrieval efficiency and outperforms various existing systems substantially. 相似文献
8.
Series feature aggregation for content-based image retrieval 总被引:1,自引:0,他引:1
Feature aggregation is a critical technique in content-based image retrieval (CBIR) systems that employs multiple visual features to characterize image content. Most previous feature aggregation schemes apply parallel topology, e.g., the linear combination scheme, which suffer from two problems. First, the function of individual visual feature is limited since the ranks of the retrieved images are determined only by the combined similarity. Second, the irrelevant images seriously affect the retrieval performance of feature aggregation scheme since all images in a collection will be ranked. To address these problems, we propose a new feature aggregation scheme, series feature aggregation (SFA). SFA selects relevant images using visual features one by one in series from the images highly ranked by the previous visual feature. The irrelevant images will be effectively filtered out by individual visual features in each stage, and the remaining images are collectively described by all visual features. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that the proposed SFA can outperform conventional parallel feature aggregation schemes. 相似文献
9.
基于内容的网络集群负载平衡算法模型 总被引:1,自引:0,他引:1
在论述网络集群负载平衡算法的基础上,基于内容分类的方法,给出基于内容的网络集群负载平衡算法三元组模型。请求分类有利于提高缓存命中率,调度机制说明如何适当地转发请求,动态反馈避免将请求分配到重载的服务器,进而分析了调度机制的八种调度策略和六种基于内容的调度转发技术。该模型利用缓存内容来提高集群的吞吐量和响应时间,可部署多种服务类型。 相似文献
10.
This paper proposes a two-stage system for text detection in video images. In the first stage, text lines are detected based on the edge map of the image leading in a high recall rate with low computational time expenses. In the second stage, the result is refined using a sliding window and an SVM classifier trained on features obtained by a new Local Binary Pattern-based operator (eLBP) that describes the local edge distribution. The whole algorithm is used in a multiresolution fashion enabling detection of characters for a broad size range. Experimental results, based on a new evaluation methodology, show the promising overall performance of the system on a challenging corpus, and prove the superior discriminating ability of the proposed feature set against the best features reported in the literature. 相似文献