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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.  ...  Contribution The primary contribution of this paper are: (i) design of a multi-feature CBIR system, (ii) automatic weight selection of individual features, (iii) extensive experiments of four publicly  ... 
arXiv:1812.04215v1 fatcat:e2qmhv7lmjfqnfodn3hhm3my4e

Survey on content based image retrieval

Ankitha Varma, Dr. Kamalpreet Kaur
2018 International Journal of Engineering & Technology  
CBIR combines features of color, texture as well as shape which ease out the process of extracting desired information from the retrieved images.  ...  This paper pre- sents a systematic and a detailed review of the CBIR method along with the different databases and evaluation parameters used for the analysis.  ...  , Dwt & In- Nidhi verse Difference Moment Tripa-Thi, A New Technique For Cbir With Con-Trast En- Uses Multi-Feature (Idm) For Textures, Skewness Focuses Pankaj Hance-Ment Us-Ing Multi-Feature And Multi-Class  ... 
doi:10.14419/ijet.v7i4.5.21136 fatcat:kfwaxjucnzbczmg7fqcuzhwpzm

Compact image signature generation: An application in image retrieval

Manish Chowdhury, Sudeb Das, Malay Kumar Kundu
2013 2013 5th International Conference on Computer Science and Information Technology  
Extensive experiments were carried out to evaluate the effectiveness of the proposed system on SIMPLIcity image database consisting of 1000 images.  ...  Experimental results and comparisons show that the proposed CBIR system performs efficiently in image retrieval domain.  ...  In this cases, weights of each component features of different planes is determined from the feature evaluation mechanism described above.  ... 
doi:10.1109/csit.2013.6588749 fatcat:phyibjzj5jctzhcogl26wbo3fq

Bridging the Feature Gaps for Retrieval of Multi-Dimensional Images

Jinman Kim, Weidong Cai, Dagan Feng
2009 International Journal of Healthcare Information Systems and Informatics  
This manuscript summarizes research in bridging the feature gap for retrieval of multi-dimensional biomedical images.  ...  Content-based image retrieval (CBIR) refers to the use of visual features of images for retrieval from an image database, and has become an attractive approach to managing medical image databases.  ...  in Fig. 2(a) ) as the functional feature. 13 Multi-dimensional biomedical CBIR 13 Feature weightings were set as 50:30:20, respectively of TTAC, location, and volume features.  ... 
doi:10.4018/jhisi.2009010103 fatcat:kytiqt5kyzdqbdsd3zlxagkjce


M Praveen, K. Sivanarulselvan, P. Betty
2016 ELK Asia Pacific Journal of Computer Science and Information Systems  
Keywords: Content based image retrieval (CBIR), Feature matching, Multiple query image retrieval (MQIR), semantic gap reduction techniques.  ...  Every image in the query set has its own representations for the query processing. For each query image in multi-query, the proper weight for similarity measurement is set.  ...  In this paper, different multi-query approaches for reducing semantic gap in CBIR systems are surveyed.  ... 
doi:10.16962/eapjcsis/issn.2394-0441/20160930.v2i1.03 fatcat:npu62nb2xfbgtkqpekux6wik54

Survey on Control of Photo Sharing on Online Social Networks

Sahla Nazlin A, Vishnu K
2017 IJARCCE  
Haar cascade classifier and CBIR (content based image retrieval) algorithm are used in the proposed system for face detection and recognition.  ...  Online Social Networks (ONS) is an important part of our everyday life...At present people are very interested to share the photo on online social networks, unluckily which is used for the purpose we not  ...  Step 2: Extract the feature vector for the input image Step 3: Calculate the weighted features vectors for the input image.  ... 
doi:10.17148/ijarcce.2017.6363 fatcat:ljikkwv5zjhaxlelfe6ya2zn7e

A Survey on Content-based Image Retrieval

Mohamed Maher
2017 International Journal of Advanced Computer Science and Applications  
The last decade has witnessed the introduction of promising CBIR systems and promoted applications in various fields.  ...  The widespread of smart devices along with the exponential growth of virtual societies yield big digital image databases.  ...  ACKNOWLEDGMENT This work was supported by the Research Centre of the College of Computer and Information Sciences, King Saud University. The author is grateful for this support.  ... 
doi:10.14569/ijacsa.2017.080521 fatcat:kzfskamd25coxcj3537z6z3ty4

A Proposed Framework for a Distributed CBIR System based on Salient Regions and RF Techniques

Ali. I.ELdesoky, Hisham A. Arafat, Noha A. Sakr
2012 International Journal of Computer Applications  
The main objective of this paper is to propose a framework for region content based image retrieval based on a distributed clustered image dataset.  ...  to the proposed third technique which is Query Modified Re-Weighting technique.  ...  In typical CBIR system, the visual contents of the images in the database are extracted and described by multi-dimensional feature vectors [3] .  ... 
doi:10.5120/8113-1730 fatcat:ngbfvbjvgfeirpfruckwettcdu

Content-Based Microscopic Image Retrieval System for Multi-Image Queries

H. C. Akakin, M. N. Gurcan
2012 IEEE Transactions on Information Technology in Biomedicine  
In this paper, we describe the design and development of a multitiered content-based image retrieval (CBIR) system for microscopic images utilizing a reference database that contains images of more than  ...  By using leave-one-slide out testing scheme, the multi-image query algorithm with the proposed weighting strategy achieves about 93% and 86% of average classification accuracy at the first rank retrieval  ...  Gerard Lozanski for insightful discussions, Robert Schmidt and Amie Draper for developing the graphical user interface.  ... 
doi:10.1109/titb.2012.2185829 pmid:22311866 pmcid:PMC3389578 fatcat:j4qtqbvhmff6pl72gbmeycnc5a

Multimodal biomedical image retrieval using hierarchical classification and modality fusion

Md Mahmudur Rahman, Daekeun You, Matthew S. Simpson, Sameer K. Antani, Dina Demner-Fushman, George R. Thoma
2013 International Journal of Multimedia Information Retrieval  
For the CBIR search, several visual features were extracted to represent the images. Modalityspecific information was used for similarity fusion and selection of a relevant image subset.  ...  To minimize limitations of low-level feature representations in content-based image retrieval (CBIR), and to complement text-based search, we propose a multi-modal image search approach that exploits hierarchical  ...  dimensional multi-modal feature vector. 7: Determine imaging modality based multi-modal feature input to the multi-class SVM classifier. 8: Perform category-specific CBIR similarity fusion in filtered  ... 
doi:10.1007/s13735-013-0038-4 fatcat:cl7yikyncjaknivblsixnjbe74

A Visual Analytics Approach Using the Exploration of Multidimensional Feature Spaces for Content-Based Medical Image Retrieval

Ashnil Kumar, Falk Nette, Karsten Klein, Michael Fulham, Jinman Kim
2015 IEEE journal of biomedical and health informatics  
Content-based image retrieval (CBIR) is a search technique based on the similarity of visual features and has demonstrated potential benefits for medical diagnosis, education, and research.  ...  for users to verify if relevant images, with a small subset of outlier features, were missed.  ...  The authors are grateful for the support given to F. Nette by Google Summer of Code.  ... 
doi:10.1109/jbhi.2014.2361318 pmid:25296409 fatcat:zwsh43n4wjg2hnyt4zy4ojv54i

Multi Wavelet for Image Retrival Based On Using Texture and Color Querys

K. Joseph Bhushanam
2012 IOSR Journal of Computer Engineering  
There has been research on texture feature extraction by finding the Spatial/frequency distribution of the patterns with tools like the Gabor filters, Pyramid-structured wavelet transform, and Multi wavelet  ...  The aim of this project is to retrieve the images using multi wavelet.  ...  Introduction Content-Based Image Retrieval (CBIR) is the process of retrieving images from a database on the basis of features that are extracted automatically from the images themselves [2] .  ... 
doi:10.9790/0661-0661013 fatcat:kl6zhfoukfetjjb77sglybect4

Region-Based Image Retrieval Using Relevance Feature Weights

Ouiem Bchir, Mohamed Maher Ben Ismail, Hadeel Aljam
2018 International Journal of Fuzzy Logic and Intelligent Systems  
More specifically, relevance weights are automatically associated with each visual feature in order to better represent the visual content of the images.  ...  Moreover, the proposed approach overcomes the challenge of choosing suitable features to describe the image content.  ...  Acknowledgements The authors are grateful for the support by the Research Center of the College of Computer and Information Sciences, King Saud University.  ... 
doi:10.5391/ijfis.2018.18.1.65 fatcat:ve7hgsh2afalhpzymixbvspbci

Adapting content-based image retrieval techniques for the semantic annotation of medical images

Ashnil Kumar, Shane Dyer, Jinman Kim, Changyang Li, Philip H.W. Leong, Michael Fulham, Dagan Feng
2016 Computerized Medical Imaging and Graphics  
In this paper, we present a method for the automatic semantic annotation of medical images that leverages techniques from content-based image retrieval (CBIR).  ...  Our method extends CBIR techniques to identify or retrieve a collection of labelled images that have similar low-level features and then uses this collection to determine the best high-level semantic annotations  ...  Acknowledgments The authors would like to thank the ImageCLEF organisers for their independent evaluation of our annotation method.  ... 
doi:10.1016/j.compmedimag.2016.01.001 pmid:26890880 fatcat:r2xgmt4655go3adcznmlhku7o4

Unsupervised Content Based Image Retrieval by Combining Visual Features of an Image With A Threshold

S. M. Zakariya, Rashid Ali, Nesar Ahmad
2012 International journal of computer and communication technology  
But, the proposed system combines all the features (shape, color, and texture) with a threshold for the purpose. The combination of all the features provides a robust feature set for image retrieval.  ...  Content-based image retrieval (CBIR) uses the visual features of an image such as color, shape and texture to represent and index the image.  ...  For a given query image, the number of neighboring target images is determined by ε. • Nearest-neighboring method (NNM) first chooses k NN of i as seeds. The r NN for each seed is then found.  ... 
doi:10.47893/ijcct.2012.1131 fatcat:ow4al2bdujfxjlydns5vl5s4xu
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