|2/2020 - 12|
Generation of Visual Patterns from BoVW for Image Retrieval using modified Similarity Score FusionARULMOZHI, P. , ABIRAMI, M.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,519 KB) | Citation | Downloads: 85 | Views: 180|
feature extraction, image fusion, image matching, image representation, supervised learning
image(36), retrieval(19), visual(14), vision(13), recognition(12), pattern(11), cvpr(11), words(9), fusion(8), classification(8)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 101 - 112
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02012
Web of Science Accession Number: 000537943500012
SCOPUS ID: 85087452294
The Bag of Visual Words (BoVW) turns up to be an efficient method to represent images for Content Based Image Retrieval (CBIR). Despite their significant usage, the traditional BoVW method has low discriminative power and fails to provide spatial information, which increases the false positive images and reduces the precision values. To address the first issue, a novel way of identifying a set of visual words unique for each category, named as Visual Patterns (VP) is proposed. Also, the weight for the respective VPs and a new way of score calculations for similarity matching with the database images are proposed. Then, to address the second issue of enhancing the spatial information, late fusion of Gabor filter features along with VP is proposed. As a consequence, VP provides better discriminative power and Gabor filtering, taking advantage of its complementary clue, provides spatial information. Hence, it helps to reduce the false matches and improves the precision values. Experiments are carried out on the popular datasets, namely, Caltech 256, Oxford 5K and Inria Holidays datasets along with Flickr 1M dataset. The proposed method is compared with other BoVW based models and proved that the MAP value is improved 0.50 times from the basic BoVW model.
|References|||||Cited By «-- Click to see who has cited this paper|
| Y. Rui, T.S. Huang, and S. F. Chang, "Image retrieval: Current techniques, promising directions, and open issues," Journal of visual communication and image representation, vol.10, no. 1, pp. 39-62, 1999. |
[CrossRef] [Web of Science Times Cited 1111]
 R. Datta, D. Joshi, J. Li and J. Z. Wang, "Image retrieval: Ideas, influences, and trends of the new age," ACM Computing Surveys (Csur), vol.40, no.2, pp.5, 2008.
[CrossRef] [Web of Science Times Cited 1530]
 M. Rehman, M. Iqbal, M. Sharif and M. Raza, "Content based image retrieval: survey," World Applied Sciences Journal, vol.19, no. 3, pp. 404-412, 2012.
 A. Alzubi, A. Amira and N. Ramzan, "Semantic content-based image retrieval: A comprehensive study," Journal of Visual Communication and Image Representation, vol.32, pp. 20-54, July 2015.
[CrossRef] [Web of Science Times Cited 76]
 W. Zhou, H. Li and Q. Tian, "Recent advance in content-based image retrieval: A literature survey," arXiv preprint arXiv: 1706.06064 2017.
 J. Sivic and A. Zisserman, "Video Google: a text retrieval approach to object matching in videos," Proceedings Ninth IEEE International Conference on Computer Vision, vol.2, pp. 1470-1477, 2003.
[CrossRef] [Web of Science Times Cited 2712]
 G. Scurka, L. Fan, C. R. Dance and C. Brey, "Visual categorization with bags of keypoints," In Workshop on statistical learning in computer vision, ECCV, vol. 1, no. 1-22, pp. 1-2, 2004.
 D. Nister and H. Stewenius, "Scalable recognition with a vocabulary tree," In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), vol. 2, pp. 2161-2168, 2006.
 H. Jegou, M. Douze, C. Schmid and P. Perez, "Aggregating local descriptors into a compact image representation," In CVPR 2010-23rd IEEE Conference on Computer Vision & Pattern Recognition, pp. 3304-3311,2010.
[CrossRef] [Web of Science Times Cited 938]
 Y. Mu, J. Sun, T. X. Han, L. F. Cheong and S. Yan, "Randomized locality sensitive vocabularies for bag-of-features model," In European Conference on Computer Vision, pp. 748-761, 2010.
 Q. Huang et al., "Building contextual visual vocabulary for large-scale image applications," In Proceedings of the 18th ACM international conference on Multimedia, ACM, pp. 501-510, 2010.
[CrossRef] [Web of Science Times Cited 69]
 N. Ali, K. Bajwa, R. Sablatnig and Z. Mehmood, "Image retrieval by addition of spatial information based on histograms of triangular regions," Computers & Electrical Engineering, vol.54, pp. 539-550, 2016.
[CrossRef] [Web of Science Times Cited 45]
 W. Li, P. Dong, B. Xiao and L. Zhou, "Object recognition based on the region of interest and optimal bag of words model," Neurocomputing, vol. 172, pp. 271-280, 2016.
 A. K. Tiwari, V. Kanhangad and R. B. Pachori, "Histogram refinement for texture descriptor based image retrieval," Signal Processing: Image Communication, vol.53, pp.73-85, 2017.
[CrossRef] [Web of Science Times Cited 13]
 A. Bolovinou, I. Pratikakis and S. Perantonis, "Bag of spatio-visual words for context inference in scene classification," Pattern Recognition, vol.46, no.3 pp. 1039-1053, 2013.
 Z. Zhou, Q. M. Jonathan Wu and X. Sun, "Encoding multiple contextual clues for partial-duplicate image retrieval," Pattern Recognition Letters, vol.109, pp.18-26, 2018.
 R. Wang, K. Ding, J. Yang and L. Xue, "A novel method for image classification based on bag of visual words," Journal of Visual Communication and Image Representation, vol. 40, pp. 24-33, 2016.
[CrossRef] [Web of Science Times Cited 8]
 W. C. Lin, C. F. Tsai, Z. Y. Chen and S. W. Ke, "Keypoint selection for efficient bag-of-words feature generation and effective image classification," Information Sciences, vol. 329, pp. 33-51, 2016.
[CrossRef] [Web of Science Times Cited 24]
 R. Khan, C. Barat, D. Muselet and C. Ducottet, "Spatial histograms of soft pairwise similar patches to improve the bag-of-visual-words model," Computer Vision and Image Understanding, vol. 132. pp. 102-112, 2015,
[CrossRef] [Web of Science Times Cited 23]
 E. Gavves, C. G. M. Snoek and A. W. M. Smeulders, "Visual synonyms for landmark image retrieval," Computer Vision and Image Understanding, vol.116, no. 2, pp. 238-249, 2012.
[CrossRef] [Web of Science Times Cited 12]
 F. B. Silva, R. Werneck, S. Goldenstein, S. Tobbone and R. Torres, "Graph-based bag-of-words for classification," Pattern Recognition, vol.74, pp. 266-285, 2018.
[CrossRef] [Web of Science Times Cited 27]
 I. Dimitrovski, D. Kocev, S. Loskovska and S. Dzeroski, "Improving bag-of-visual-words image retrieval with predictive clustering trees," Information Sciences, vol. 329, pp. 851-865, 2016.
[CrossRef] [Web of Science Times Cited 24]
 C. G. M. Snoek, M. Worring and A. W. M. Smeulders, "Early versus late fusion in semantic video analysis," In Proceedings of the 13th annual ACM international conference on Multimedia, pp. 399-402, 2005.
 P. A. S. Kimura, J. M. B. Cavalcanti, P. S. Saraiva, R. S. Torres and M. A. Goncalves, "Evaluating retrieval effectiveness of descriptors for searching in large image databases," Journal of information and data management vol.2, no. 3, 2011.
 M. A. Herraez, J. Domingo and F. J. Ferri, "Combining similarity measures in content-based image retrieval," Pattern Recognition Letters, vol.29, no. 16, pp. 2174-2181, 2008.
 H. J. Escalante, C. A. Hernadez, L. E. Sucar and M. Montes, "Late fusion of heterogeneous methods for multimedia image retrieval," In Proceedings of the 1st ACM international conference on Multimedia information retrieval, pp. 172-179, 2008.
 L. Piras and G. Giacinto, "Information fusion in content based image retrieval: A comprehensive overview," Information Fusion, vol. 37, pp. 50-60, 2017.
[CrossRef] [Web of Science Times Cited 38]
 Y. Xu and Y. Lu, "Adaptive weighted fusion: A novel fusion approach for image classification," Neurocomputing, vol.168, pp. 566-574, 2015.
[CrossRef] [Web of Science Times Cited 41]
 C. Jun, J. Shao, X. Xu, D. Ouyang and L. Gao, "Exploiting score distribution for heterogenous feature fusion in image classification," Neurocomputing, vol. 253, pp. 70-76, 2017.
 Z. Liang et al., "Query-adaptive late fusion for image search and person re-identification," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1741-1750, 2015.
 G. Griffin, A. Holub and P. Perona, "Caltech-256 object category dataset," 2007.
 J. Philbin, O. Chum, M. Isard, J. Sivic and A. Zisserman, "Object retrieval with large vocabularies and fast spatial matching," In 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2007.
[CrossRef] [Web of Science Times Cited 942]
 H. Jegou, M. Douze and C. Schmid, "Hamming embedding and weak geometric consistency for large scale image search," In European conference on computer vision, Springer, Berlin, pp. 304-317, 2008.
 H. Jegou, M. Douze and C. Schmid, "Improving bag-of-features for large scale image search," International journal of computer vision, vol. 87, no. 3, pp. 316-336, 2010.
 J. M. Dos Santos, E. S. De Moura, A. S. De Silva and R. S. Torres, "A signature-based bag of visual words method for image indexing and search," Pattern Recognition Letters, vol. 65, pp. 1-7, 2015.
[CrossRef] [Web of Science Times Cited 6]
 F. Perronnin et al., "Aggregating local image descriptors into compact codes," IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 9, pp. 1704-1716, 2011.
 Y. Zhang, Z. Jia and T. Chen, "Image retrieval with geometry-preserving visual phrases," In CVPR 2011, pp. 809-816, 2011.
 C. Yang, C. Wang, Z. Li and L. Zhang, "Spatial-bag-of-features," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3352-3359, 2010.
[CrossRef] [Web of Science Times Cited 110]
 G. Amato, F. Falchi and L. Vadicamo, "Aggregating binary local descriptors for image retrieval," Multimedia Tools and Applications, vol. 77, no. 5, pp. 5385-5415, 2018.
[CrossRef] [Web of Science Times Cited 3]
 R. Wang, K. Ding, J. Yang and L. Xue, "A novel method for image classification based on bag of visual words," Journal of Visual Communication and Image Representation, vol.40 pp. 24-33, 2016.
[CrossRef] [Web of Science Times Cited 8]
 J. Yang, K. Yu, Y. Gong and T.S. Huang, "Linear spatial pyramid matching using sparse coding for image classification," CVPR, vol. 1, no. 2, pp. 6, 2009.
 M. D. Zeiler and R. Fergus, "Visualizing and understanding convolutional networks," In European conference on computer vision, pp.818-833, 2014.
Web of Science® Citations for all references: 7,760 TCR
SCOPUS® Citations for all references: 0
Web of Science® Average Citations per reference: 180 ACR
SCOPUS® Average Citations per reference: 0
TCR = Total Citations for References / ACR = Average Citations per Reference
We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more
Citations for references updated on 2020-09-29 00:10 in 239 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.