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Object-based Image Retrieval using Local Feature Extraction and Relevance Feedback

by Mario H. G. Freitas, Flavio L. C. Padua, Guilherme T. Assis
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 78 - Number 7
Year of Publication: 2013
Authors: Mario H. G. Freitas, Flavio L. C. Padua, Guilherme T. Assis
10.5120/13499-1239

Mario H. G. Freitas, Flavio L. C. Padua, Guilherme T. Assis . Object-based Image Retrieval using Local Feature Extraction and Relevance Feedback. International Journal of Computer Applications. 78, 7 ( September 2013), 8-14. DOI=10.5120/13499-1239

@article{ 10.5120/13499-1239,
author = { Mario H. G. Freitas, Flavio L. C. Padua, Guilherme T. Assis },
title = { Object-based Image Retrieval using Local Feature Extraction and Relevance Feedback },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 78 },
number = { 7 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 8-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume78/number7/13499-1239/ },
doi = { 10.5120/13499-1239 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:50:58.124073+05:30
%A Mario H. G. Freitas
%A Flavio L. C. Padua
%A Guilherme T. Assis
%T Object-based Image Retrieval using Local Feature Extraction and Relevance Feedback
%J International Journal of Computer Applications
%@ 0975-8887
%V 78
%N 7
%P 8-14
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper addresses the problem of object-based image retrieval, by using local feature extraction and a relevance feedback mechanism for quickly narrowing down the image search process to the user needs. This approach relies on the hypothesis that semantically similar images are clustered in some feature space and, in this scenario: (i) computes image signatures that are invariant to scale and rotation using SIFT, (ii) calculates the vector of locally aggregated descriptors (VLAD) to make a fixed length descriptor for the images, (iii) reduce the VLAD descriptor dimensionality with Principal Component Analysis (PCA) and (iv) uses the k-Means algorithm for grouping images that are semantically similar. The proposed approach has been successfully validated using 33,192 images from the ALOI database, obtaining a mean recall value of 47. 4% for searches of images containing objects that are identical to the object query and 20. 7% for searches of images containing different objects (albeit visually similar) to the object query.

References
  1. Bay, H. , Ess, A. , Tuytelaars, T. , and Van Gool, L. 2008. Speeded-up robust features (SURF). Computer vision and image understanding, 110(3), 346-359.
  2. Carneiro, G. , and Lowe, D. 2006. Sparse flexible models of local features. In Proceedings of European Conference on Computer Vision (ECCV), pp. 29-43.
  3. Datta, R. , Joshi, D. , Li, J. , and Wang, J. Z. 2008. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (CSUR), 40(2), 5.
  4. Duan, F. , Li, X. , Liu, J. , and Xie, K. 2007. Image Retrieval Model Based on Immune Algorithm. In Proceedings of IEEE Workshop on Intelligent Information Technology Application, pp. 141-144.
  5. Dunn, J. C. 1973. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(1), 32–57.
  6. Flickner, M. , Sawhney, H. , Niblack, W. , Ashley, J. , Huang, Q. , Dom, B. , and Yanker, P. 1995. Query by image and video content: the QBIC system. Computer, 28(9), 23-32.
  7. Geusebroek, J. , Burghouts, G. J. , and Smeulders, A. W. 2005. The Amsterdam library of object images. International Journal of Computer Vision, 61(1), 103-112.
  8. Gevers, T. , and Smeulders, A. W. 2000. Pictoseek: Combining color and shape invariant features for image retrieval. Image Processing, IEEE Transactions on, 9(1), 102-119.
  9. Jégou, H. , Perronnin, F. , Douze, M. , and Schmid, C. 2012. Aggregating local image descriptors into compact codes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(9), 1704-1716.
  10. Jeong, S. , Won, C. S. , and Gray, R. M. 2004. Image retrieval using color histograms generated by Gauss mixture vector quantization. Computer Vision and Image Understanding, 94(1), 44-66.
  11. Liu, Y. , Zhang, D. , Lu, G. , and Ma, W. Y. 2007. A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40(1), 262-282.
  12. Lowe, D. G. 2004. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110.
  13. MacQueen, J. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1, No. 281-297, p. 14.
  14. Pearson, K. 1901. LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572.
  15. Perronnin, F. , Dance, C. 2007. Fisher kernels on visual vocabularies for image categorization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8.
  16. Petrelli, D. and Auld, D. 2008. An examination of automatic video retrieval technology on access to the contents of an historical video archive. Program: electronic library and information systems, 42(2), 115-136.
  17. Sivic, J. , Zisserman, A. 2003. Video Google: A text retrieval approach to object matching in videos. In Proceedings of the IEEE International Conference on Computer Vision, pp. 1470-1477.
  18. Smeulders, A. W. , Worring, M. , Santini, S. , Gupta, A. , and Jain, R. 2000. Content-based image retrieval at the end of the early years. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(12), 1349-1380.
  19. Sukthankar, R. , and Ke, Y. 2004. PCA–SIFT: A more distinctive representation for local image descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 506–513.
  20. Suruchi B. , and Shahane, N. M. 2013. A survey on textured based CBIR techniques. In IJCA Proceedings on International Conference on Recent Trends in Engineering and Technology, pp. 11-14.
  21. Wang, M. , Ye, Z. , Wang, Y. , and Wang, S. 2008. Dominant sets clustering for image retrieval. Signal Processing, 88 (11) , pp. 2843–2849.
Index Terms

Computer Science
Information Sciences

Keywords

Object-based image retrieval scale invariant feature transform principal component analysis vector of locally aggregated descriptors clustering algorithms

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