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Image Classification using Frequent Itemset Mining

by Vyoma Patel, G. J. Sahani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 15
Year of Publication: 2015
Authors: Vyoma Patel, G. J. Sahani
10.5120/21614-4880

Vyoma Patel, G. J. Sahani . Image Classification using Frequent Itemset Mining. International Journal of Computer Applications. 121, 15 ( July 2015), 7-11. DOI=10.5120/21614-4880

@article{ 10.5120/21614-4880,
author = { Vyoma Patel, G. J. Sahani },
title = { Image Classification using Frequent Itemset Mining },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 15 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number15/21614-4880/ },
doi = { 10.5120/21614-4880 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:08:29.655072+05:30
%A Vyoma Patel
%A G. J. Sahani
%T Image Classification using Frequent Itemset Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 15
%P 7-11
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image classification is one of the most useful and essential research field in computer vision domain and challenging task in the image management and retrieval system. The growing demands for image classification in computer vision having application such as video surveillance, image and video retrieval, web content analysis, biometrics etc. have pushed application developers to search and classify images more efficiently. The main goal of image classification is to classifying image into different classes according to their visual characteristics. In this paper, we propose an approach for image classification by applying frequent itemset mining. Frequent Itemset Mining is used to finding the frequent patterns which is referring as Frequent Local Histograms or FLHs. All Local Histogram information must be maintained for obtaining these FLHs patterns and demonstrate the Bag-of-FLHs based image representation. The main aim of this research is to use PCA-SIFT (Principal Component Analysis- Scale Invariant Feature Transform) local descriptor for feature extraction. It is more distinctive, robust to image deformations and more compact than the standard SIFT descriptor. The proposed work reduces the overall time of image classification task and maintaining its overall accuracy.

References
  1. Agrawal, R. , Srikant, R. : Fast algorithms for mining association rules in large databases. In: VLDB, pp. 487–499 (1994).
  2. Kim, S. , Jin, X. , Han, J. : Disiclass: discriminative frequent pattern-based image classification. In: Tenth Int. Workshop on Multimedia Data Mining (2010).
  3. Fernando, B. , Fromont, É. , and Tuytelaars, T. " Effective use of frequent itemset mining for image classification. " Computer Science-Springer, Volume. 7572, 214–227,2012.
  4. Fernando, B. , Fromont, É. , and Tuytelaars, T. " Mining Mid-level Features for Image Classification. " International Journal of Computer Vision-Springer, Volume. 108, 186-203, 2014.
  5. Vyoma Patel and G. J. Sahani. "A Survey on Image Classification using Data Mining Techniques. " International Journal for Scientific Research & Development, Volume. 2, Issue 10, 2014.
  6. Cheng, H. , Yan, X. , Han, J. , Hsu, C. W. : Discriminative frequent pattern analysis for effective classification. In: ICDE, pp. 716–725 (2007).
  7. David, G. L. "Distinctive Image Features from Scale-Invariant Keypoints. " International Journal of Computer Vision-Springer, Volume. 60, Issue 2, 91–110,2004.
  8. Ke, Y. , Sukthanka, R. "PCA-SIFT: A More Distinctive Representation for Local Image Descriptors. " Computer Vision and Pattern Recognition-IEEE, Volume. 2, 506-513, 2004.
  9. Juan, L. ,Gwun, O. "A Comparison of SIFT, PCA-SIFT and SURF. " International Journal of Image Processing, Volume. 3, Issue 4, 187-245, 2009.
  10. Lin S. D. , Lin J. , and Chiang C. "Using gradient features from scale-invariant keypoints on face recognition. " International Journal of Innovative Computing, Information and Control, Volume. 7, 2011.
  11. Csurka, G. , Dance, C. , Fan, L. , Willamowski, J. , Bray, C. "Visual categorization with bags of keypoints. " International Workshop on Statistical Learning in Computer Vision, ECCV, 1–22, 2004.
  12. Quack, T. , Ferrari, V. , Leibe, B. , Van Gool, L. "Efficient mining of frequent and distinctive feature configurations. " IEEE 11th International Conference on Computer Vision, 1 – 8, 2007.
  13. Yuan, J. , Wu, Y. , Yang, M. : "Discovery of collocation patterns: from visual words to visual phrases. " In: CVPR (2007).
  14. Nowozin, S. , Tsuda, K. , Uno, T. , Kudo, T. , Bakir, G. "Weighted substructure mining for image analysis. " Computer Vision and Pattern Recognition-IEEE, 1-8, 2007.
  15. Uno, T. , Asai, T. , Uchida, Y. , Arimura, H. : Lcm: An efficient algorithm for enumerating frequent closed item sets. In: FIMI (2003).
  16. Yao, B. , Fei-Fei, L. : Grouplet: A structured image representation for recognizing human and object interactions. In: CVPR (2010).
  17. Svetlana Lazebnik, C. S. , Ponce, J. : Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169–2178 (2006).
  18. Image Dataset of Oxford Flowers 17 http://www. robots. ox. ac. uk/~vgg/data/flowers/17/index. html
  19. Chih-Chung Chang and Chih-Jen Lin, "LIBSVM -- A Library for Support Vector Machines", http://www. csie. ntu. edu. tw/~cjlin/libsvm/.
Index Terms

Computer Science
Information Sciences

Keywords

SIFT PCA-SIFT BOW

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