Search - CBIR relevance feedback

Search - CBIR relevance feedback - List

[Linux-Unix] gift-0.1.14.tar DL : 0: The GIFT (the GNU Image-Finding Tool) is a Content Based Image Retrieval System (CBIRS: http://en.wikipedia.org/wiki/CBIR). It enables you to do Query By Example (QBE: http://en.wikipedia.org/wiki/QBE) on images, giving you the opportunity to improve query results by relevance feedback. For processing your queries the program relies entirely on the content of the images, freeing you from the need to annotate all images before querying the collection.
Update : 2025-02-17 Size : 775kb Publisher : yudaxia
[File Format] CBIR DL : 0: Graph-Cut Transducers for Relevance Feedback in Content Based Image Retrieval, Relevance Feedback for Content-Based Image Retrieval Using Bayesian Network,CONTENT BASED IMAGE RETRIEVAL,Relevance Feedback,A survey of browsing models for content based image retrieval,Analysis of Relevance Feedback in Content Based Image Retrieval, Performance Evaluation in Content-Based Image Retrieval: Overview and Proposals, COMPARISON OF TECHNIQUES FOR CONTENT-BASED IMAGE RETRIEVAL
Update : 2025-02-17 Size : 3.3mb Publisher : sunda
[Mathimatics-Numerical algorithms] CBIR_Relevance DL : 0: 该代码为基于内容的图像检索系统CBIR中相关反馈的代码，相关反馈可以提高检索的查全查准率-The code is content-based image retrieval system, relevance feedback CBIR in the code, relevance feedback can improve the retrieval recall precision
Update : 2025-02-17 Size : 11kb Publisher : vivi
[Special Effects] CBIR-FOR-ENDOSCOPIC-IMAGES DL : 0: Content-based medical image retrieval is now getting more and more attention in the world, a feasible and efficient retrieving algorithm for clinical endoscopic images is urgently required. Methods: Based on the study of single feature image retrieving techniques, including color clustering, color texture and shape, a new retrieving method with multi-features fusion and relevance feedback is proposed to retrieve the desired endoscopic images. Results: A prototype system is set up to evaluate the proposed method’s performance and some evaluating parameters such as the retrieval precision & recall, statistical average position of top 5 most similar image on various features, etc. are therefore given. Conclusions: The algorithm with multi-features fusion and relevance feedback gets more accurate and quicker retrieving capability than the one with single feature image retrieving technique due to its flexible feature combination and interactive relevance feedback.
Update : 2025-02-17 Size : 351kb Publisher : gokul/goks
[AI-NN-PR] CBIR DL : 0: 论文《A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization》的代码-the code of <A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization>
Update : 2025-02-17 Size : 9.31mb Publisher : 林先炎
[Documents] CBIR DL : 0: 基于内容的图像检索基于颜色、纹理、形状的图像检索基于区域的图像检索基于语义的图像检索相关反馈 -Based on the content-based image retrieval based on color, texture, shape-based image retrieval region-based image retrieval based on semantic image retrieval relevance feedback
Update : 2025-02-17 Size : 5.26mb Publisher : liuyi
[Other] The-X-ray-Chest-Image-Retrieval-Based-on-Feature- DL : 0: Based on the analysis of methods of CBIR and chest image characteristic, in this paper, color correlogam, dominant color of partition, gray level co-occurrence matrix, gray-gradient co-occurrence matrix and shape invariant moments were extracted as retrieval feature. After comparison of their retrievals, feature fusion and relevance feedback is proposed. Experiments proved that the combining color, texture with shape feature gets effective retrieval and relevance feedback further more improves retrie
Update : 2025-02-17 Size : 879kb Publisher : Salkoum
[Industry research] article DL : 0: Nowadays, Content-Based Image Retrieval (CBIR) is the mainstay of image retrieval systems. To understand the query semantics and users expectations so as to communicate faithful results in terms of accuracy, Relevance Feedback (RF) was incorporated to CBIR systems. By allowing the user to assess iteratively the answers as relevant/irrelevant or even giving him/her the opportunity to specify a degree of relevance (user’s feedbacks) , the system creates a new query that better captures the user s needs, hence raising the opportunity to get more relevant image results. In this paper, we have focused on CBIR and basic concepts pertaining to it, as well as Relevance Feedback and its various mechanisms. An important contribution in this work is a comparative analysis of CBIR systems using reference feedback: major models and approaches are discussed in detail from early heuristic methods to recently optimal learning algorithms, with more emphasize on their advantages and weaknesses.-Nowadays, Content-Based Image Retrieval (CBIR) is the mainstay of image retrieval systems. To understand the query semantics and users expectations so as to communicate faithful results in terms of accuracy, Relevance Feedback (RF) was incorporated to CBIR systems. By allowing the user to assess iteratively the answers as relevant/irrelevant or even giving him/her the opportunity to specify a degree of relevance (user’s feedbacks) , the system creates a new query that better captures the user s needs, hence raising the opportunity to get more relevant image results. In this paper, we have focused on CBIR and basic concepts pertaining to it, as well as Relevance Feedback and its various mechanisms. An important contribution in this work is a comparative analysis of CBIR systems using reference feedback: major models and approaches are discussed in detail from early heuristic methods to recently optimal learning algorithms, with more emphasize on their advantages and weaknesses.
Update : 2025-02-17 Size : 263kb Publisher : ghoualmi
[Industry research] derniere-version-maroc DL : 0: In the current decade, we are witnessing a great interest in Content Based Image Retrieval (CBIR) together with a wealth of promising technologies, paved for a large number of new mechanisms and systems. In terms of mechanisms, a strong trend towards the employment of diverse Relevance Feedback (RF) approaches in CBIR systems to capture image(s) of interest has emerged. However, the need to select a particular technique in a given application domain depends on the nature of images in the collection at hand. So our paper mainly reviews and compares different approaches of CBIR using RF. Its ultimate goal is to present information about image database aspects and image features setting so as to support the selection of the appropriate CBIR with RF Techniques.
Update : 2025-02-17 Size : 258kb Publisher : ghoualmi
[Program doc] cbir DL : 0: Survey about CBIR using Relevance Feedback
Update : 2025-02-17 Size : 276kb Publisher : ghoualmi