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IPAL at ImageClef 2007 Mixing Features, Models and Knowledge

Sheng Gao, Jean-Pierre Chevallet, Diem Thi Hoang Le, Trong-Ton Pham, Joo-Hwee Lim
2007 Conference and Labs of the Evaluation Forum  
We also have used external knowledge like Wordnet, and Wikipedia for document expansion. Then the cross-modality pseudo-relevance feedback is applied to boost each individual modality.  ...  Compare with our results in 2006, our results are significantly enhanced by extracting multiple low-level visual content descriptors and fusing multiple CBIR.  ...  A "+" means the indexing method is available for the feature Pseudo-relevance Feedback Learned from the ImageClef 2006 [9] , the cross-modality pseudo-relevance feedback (PRF) can improve the system  ... 
dblp:conf/clef/GaoCLPL07 fatcat:bwz42553onbcribe3chriguwxi

Improving Retrieval Quality Using Pseudo Relevance Feedback in Content-Based Image Retrieval

Dinesha Chathurani Nanayakkara Wasam Uluwitige, Timothy Chappell, Shlomo Geva, Vinod Chandran
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy.  ...  This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools.  ...  CONCLUSIONS In this paper a RB-PRF approach is described for the application of pseudo relevance feedback in CBIR.  ... 
doi:10.1145/2911451.2914747 dblp:conf/sigir/UluwitigeCGC16 fatcat:ohil4r6xnvcinn5uqdjzdiw42i

Aggregation of Multiple Pseudo-Relevance Feedbacks for Image Search Reranking

Wei-Chao Lin
2019 IEEE Access  
INDEX TERMS Image retrieval, re-ranking, pseudo relevance feedback, Borda count.  ...  Image retrieval effectiveness can be improved by pseudo relevance feedback (PRF), which automatically uses top-k images of the initial retrieval result as the pseudo feedback.  ...  Next, top-k pseudo relevance feedback from retrieval list 1 is performed with the Rocchio algorithm.  ... 
doi:10.1109/access.2019.2942142 fatcat:jqucuyx4vrcqpg7jzv7vyv3ndu

Distance selection based on relevance feedback in the context of CBIR using the SFS meta-heuristic with one round

Mawloud Mosbah, Bachir Boucheham
2017 Egyptian Informatics Journal  
We specifically propose a hybrid system based on the Sequential Forward Selector (SFS) metaheuristic with one round and relevance feedback.  ...  Instead of addressing features' selection issue, we deal here with distance selection as a novel paradigm poorly addressed within CBIR field.  ...  For the features weighting based on the relevance feedback information, the task is done in deep way.  ... 
doi:10.1016/j.eij.2016.09.001 fatcat:tma7o32dsfb5pbsxpvyuyro5bq

A Four-Factor User Interaction Model for Content-Based Image Retrieval [chapter]

Haiming Liu, Victoria Uren, Dawei Song, Stefan Rüger
2009 Lecture Notes in Computer Science  
In order to bridge the "Semantic gap", a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR).  ...  Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system.  ...  In our view, one of the reasons for this is that CBIR is normally performed by computing the dissimilarity between objects and queries based on their multidimensional feature vectors in content feature  ... 
doi:10.1007/978-3-642-04417-5_29 fatcat:wfeltirpwfhanf5jmloy7duqty

An Efficient and Effective Image Retrieval System on the basis of (Feature, Matching Measure and sub-space) Selection

Mawloud Mosbah
2018 Journal of Information and Organizational Sciences  
The selection relies on relevance feedback information injected by the user. The approach is tested on Corel-1Kimages database. The obtained results are very promising.  ...  Since its appearance as a research field, Content-based Image Retrieval (CBIR) system has increasingly received an important attention.  ...  The pseudo code for selecting the best configuration (feature, matching measure) is given as follows: Step1: as initialization, the algorithm starts with the following weighting (0, 0,.., 0) for the features  ... 
doi:10.31341/jios.42.2.4 fatcat:vtoiqmydqjeitl2kthhurr4e6e

Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval [article]

Asheet Kumar, Shivam Choudhary, Vaibhav Singh Khokhar, Vikas Meena, Chiranjoy Chattopadhyay
2018 arXiv   pre-print
In this paper, we propose a novel CBIR framework by applying to index using multiclass SVM and finding the appropriate weights of the individual features automatically using the relevance ratio and mean  ...  During retrieval, feature vectors of query image are combined, weighted and compared with feature vectors of images in the database to rank order the results.  ...  A comparative study of major challenges in RF for CBIR is given in [14] . Authors of [15] have proposed a long term learning scheme in relevance feedback for CBIR.  ... 
arXiv:1812.04215v1 fatcat:e2qmhv7lmjfqnfodn3hhm3my4e

The effects of multiple query evidences on social image retrieval

Zhiyong Cheng, Jialie Shen, Haiyan Miao
2014 Multimedia Systems  
We use a pseudo relevance feedback method to expand the text query with social tags.  ...  Besides, we also study the effects of automatic text query expansion with social tags using a pseudo relevance feedback method on the retrieval performance.  ... 
doi:10.1007/s00530-014-0432-7 fatcat:m6en7vwiv5cdxiphuj3mwozi4m

Towards Data-Adaptive and User-Adaptive Image Retrieval by Peer Indexing

Jun Yang, Qing Li, Yueting Zhuang
2004 International Journal of Computer Vision  
A cooperative framework is proposed under which peer indices and image visual features are integrated to facilitate data-and user-adaptive image retrieval.  ...  Based on two-level image peer indices, retrieval parameters including query vectors and similarity metric can be optimized towards both data and user characteristics by applying the pseudo feedback strategy  ...  The authors would like to express their thanks to Dr.Liu Wenyin for a fruitful discussion on the issue of user personalization, which helped our presentation on the user-adaptation aspect of this paper  ... 
doi:10.1023/b:visi.0000004836.59343.e9 fatcat:pfgkrtedhbfv3eeru2gkcgab6y

Relevance Feedback and Term Weighting Schemes for Content-Based Image Retrieval [chapter]

David Squire, Wolfgang Müller, Henning Müller
1999 Lecture Notes in Computer Science  
Specically, the use of inverted les, frequency-based weights and relevance feedback is investigated.  ...  Several weighting schemes used in text retrieval are employed, yielding varying results. We suggest possible modications for their use with image databases.  ...  Conclusion We have shown how techniques used in TR (inverted les, relevance feedback and term weighting) can be adapted for use in CBIR.  ... 
doi:10.1007/3-540-48762-x_68 fatcat:cokjhqv7pffjdpj5ktq2z3ro6i

Evaluating multimodal relevance feedback techniques for medical image retrieval

Dimitrios Markonis, Roger Schaer, Henning Müller
2015 Information retrieval (Boston)  
Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use.  ...  A novel relevance feedback technique that uses both text and visual information of the results is proposed.  ...  However it is only evaluated with respect to the size of the shortlist used for the pseudo-relevance feedback and not against other relevance feedback techniques.  ... 
doi:10.1007/s10791-015-9260-4 fatcat:fbp7rtqpt5euvpptl4q2xyqihq

Content Based Image Retrieval System using Feature Classification with Modified KNN Algorithm [article]

T. Dharani, I. Laurence Aroquiaraj
2013 arXiv   pre-print
Feature means countenance, remote sensing scene objects with similar characteristics, associated to interesting scene elements in the image formation process.  ...  MKNN contains two parts for processing, they are validity of the train samples and applying weighted KNN. The validity of each point is computed according to its neighbors.  ...  Processing of query (image or graphics) involves extraction of visual features and/or segmentation and search in the visual feature space for similar images. Composite.  ... 
arXiv:1307.4717v1 fatcat:f5drtl7kpfe5rdbvkfp4qus5ya

A Comparative Study of Dimension Reduction Techniques for Content-Based Image Retrivel

Sasikala G, Kowsalya R, Punithavalli M
2010 The International Journal of Multimedia & Its Applications  
Content-based image retrieval is a promising approach because of its automatic indexing and retrieval based on their semantic features and visual appearance.  ...  It is designed for discovering the local manifold structure. Therefore, MMP is likely to be more suitable for image retrieval systems, where nearest neighbor search is usually involved.  ...  Relevance feedback is one of the most important techniques for narrowing down the gap between low-level visual features and high-level semantic concepts [3] .  ... 
doi:10.5121/ijma.2010.2303 fatcat:eebfcnngjfgerahgxq3zzuvyiq

A Pseudo-Labeling Framework for Content-based Image Retrieval

Kim-Hui Yap, Kui Wu
2007 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing  
One of the main issues associated with relevance feedback in CBIR systems is the small sample problem where only a limited number of labeled samples are available for learning.  ...  This is because image labeling is time consuming and users are often reluctant to label too many images for feedback.  ...  The selection criterion is to determine certain informative samples among the unlabeled ones which are 'similar' to the labeled images in terms of the visual features for pseudo-labeling and fuzzy membership  ... 
doi:10.1109/ciisp.2007.369179 fatcat:wi4kmdqwqzctdnsmt3nwde7w7a

A Relevance Feedback Image Retrieval Scheme Using Multi-Instance and Pseudo Image Concepts

F.-C. CHANG
2006 IEICE transactions on information and systems  
In realizing the preceding concepts, an image search scheme is developed using the weighted low-level image features.  ...  The second key concept is that when the user does not provide a sufficient number of samples, how we generate a set of consistent "pseudo images".  ...  In a typical Query-by-Example (QBE) CBIR system with relevance feedback function, it analyzes the user query images and/or relevant feedback images to derive the search parameters.  ... 
doi:10.1093/ietisy/e89-d.5.1720 fatcat:ehj4dykoefdgphnggfdzfxtubu
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