Description: 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. Platform: |
Size: 793600 |
Author:yudaxia |
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Description: 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 Platform: |
Size: 3459072 |
Author:sunda |
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Description: 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. Platform: |
Size: 359424 |
Author:gokul/goks |
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Description: 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 Platform: |
Size: 900096 |
Author:Salkoum |
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Description: 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. Platform: |
Size: 269312 |
Author:ghoualmi |
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Description: 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. Platform: |
Size: 264192 |
Author:ghoualmi |
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Search - CBIR relevance feedback