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Abstract

Due to the fast growth of multimedia archives, the semantic gap is becoming a vital problem between machine learning based semantic concepts and local features of the image to retrieve images accurately. To address this issue, the proposed method of this article introduces two novel methods for effective image retrieval known as visual words integration after clustering (VWIaC) and feature integration before clustering (FIbC). These methods use complementary features of histograms of oriented gradients (HOG) and oriented FAST and rotated BRIEF (ORB) descriptors founded on the bag-of-words (BoW) model for salient objects within the images to build smaller and larger sizes of codebooks. To achieve higher efficiency in terms of specificity of the image retrieval system, the codebook of larger sizes are preferred, while larger sizes codebook produces low sensitivity and vice versa. The proposed method of VWIaC produces two smaller sizes codebooks to achieve higher sensitivity. After that visual words of both smaller size codebooks are integrated to produce larger size codebook, which improves the specificity of the proposed method. The performance of the proposed method is tested on three standard image benchmarks, which verifies its vigorous performance as compared to an FIbC method and recent CBIR methods.

Keywords

Image visuals search complementary image visuals object retrieval clustering complementary features

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References

Yu ,

Yang ,

Yao ,

Sun and

Xu , Exploiting the complementary strengths of multi-layer CNN features for image retrieval, Neurocomputing 237 (2017), 235–241.

Mei ,

Rui ,

Li and

Tian , Multimedia search reranking: A literature survey, ACM Computing Surveys (CSUR) 46 (2014), 38.

Datta ,

Joshi ,

Li and

J.Z.

Wang , Image retrieval: Ideas, influences, and trends of the new age, ACM Computing Surveys (Csur) 40 (2008), 5.

Gagaudakis and

P.L.

Rosin , Incorporating shape into histograms for CBIR, Pattern Recognition 35 (2002), 81–91.

Margolin ,

Tal and

Zelnik-Manor , What makes a patch distinct? in Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, 2013, pp. 1139–1146.

Ko ,

H.-S.

Lee and

Byun , Image retrieval using flexible image subblocks, in Proceedings of the 2000 ACM Symposium on Applied Computing-Volume 2, 2000, pp. 574–578.

Dalal and

Triggs , Histograms of oriented gradients for human detection, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, pp. 886–893.

Rublee ,

Rabaud ,

Konolige and

Bradski , ORB: An efficient alternative to SIFT or SURF, in Computer Vision (ICCV), 2011 IEEE International Conference on, 2011, pp. 2564–2571.

G.-H.

Cha , Kernel principal component analysis for content based image retrieval, in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2005, pp. 844–849.

10.

Arthur and

Vassilvitskii , k-means++: The advantages of careful seeding, in Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2007, pp. 1027–1035.

11.

Yuan ,

Yu ,

Qin and

Wan , A SIFT-LBP image retrieval model based on bag of features, in IEEE International Conference on Image Processing, 2011.

12.

Ali ,

K.B.

Bajwa ,

Sablatnig and

Mehmood , Image retrieval by addition of spatial information based on histograms of triangular regions, Computers & Electrical Engineering 54 (2016), 539–550.

13.

Mehmood ,

S.M.

Anwar and

Altaf , A novel image retrieval based on rectangular spatial histograms of visual words, Kuwait Journal of Science 45 (2018), 54–69.

14.

Sarwar ,

Mehmood ,

Saba ,

K.A.

Qazi ,

Adnan and

Jamal , A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine, Journal of Information Science (2018), 0165551518782825.

15.

Sharif ,

Mehmood ,

Mahmood ,

M.A.

Javid ,

Rehman and

Saba , Scene analysis and search using local features and support vector machine for effective content-based image retrieval, Artificial Intelligence Review (2018), 1–25.

16.

Jabeen ,

Mehmood ,

Mahmood ,

Saba ,

Rehman and

M.T.

Mahmood , An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model, PloS One 13 (2018), e0194526.

17.

Jiang ,

H.-M.

Hu ,

Zheng and

Li , A hierarchal BoW for image retrieval by enhancing feature salience, Neurocomputing 175 (2016), 146–154.

18.

Zhao ,

Zhang and

Sun , A novel image retrieval method based on multi-trend structure descriptor, Journal of Visual Communication and Image Representation 38 (2016), 73–81.

19.

Singh and

K.P.

Kaur , A fast and efficient image retrieval system based on color and texture features, Journal of Visual Communication and Image Representation 41 (2016), 225–238.

20.

Wu ,

Feng ,

Liu and

Sun , Image retrieval framework based on texton uniform descriptor and modified manifold ranking, Journal of Visual Communication and Image Representation 49 (2017), 78–88.

21.

Alzu’bi ,

Amira and

Ramzan , Content-based image retrieval with compact deep convolutional features, Neurocomputing 249 (2017), 95–105.

22.

Sajjad ,

Ullah ,

Ahmad ,

Abbas ,

Rho and

S.W.

Baik , Integrating salient colors with rotational invariant texture features for image representation in retrieval systems, Multimedia Tools and Applications 77(4) (2018), 4769–4789.

23.

Harris and

Stephens , A combined corner and edge detector, in Alvey Vision Conference, 1988, p. 10.5244.

24.

Mehmood ,

Mahmood and

M.A.

Javid , Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machine, Applied Intelligence 48 (2018), 166–181.

25.

Mehmood ,

S.M.

Anwar ,

Ali ,

H.A.

Habib and

Rashid , A novel image retrieval based on a combination of local and global histograms of visual words, Mathematical Problems in Engineering 2016 (2016), 1–12.

26.

Goldberg and

Elhadad , splitSVM: Fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications, in Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers, 2008, pp. 237–240.

27.

Wang ,

Zhang and

Feng , On the Euclidean distance of images, IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2005), 1334–1339.

28.

Yousuf ,

Mehmood ,

H.A.

Habib ,

Mahmood ,

Saba ,

Rehman , et al., A novel technique based on visual words fusion analysis of sparse features for effective content-based image retrieval, Mathematical Problems in Engineering 2018 (2018).

29.

Mehmood ,

Abbas ,

Mahmood ,

M.A.

Javid ,

Rehman and

Nawaz , Content-based image retrieval based on visual words fusion versus features fusion of local and global features, Arabian Journal for Science and Engineering, 2018, pp. 1–20.

30.

Mehmood ,

Gul ,

Altaf ,

Mahmood ,

Saba ,

Rehman , et al., Scene search based on the adapted triangular regions and soft clustering to improve the effectiveness of the visual-bag-of-words model, EURASIP Journal on Image and Video Processing 2018 (2018), 48.

31.

Li and

J.Z.

Wang , Automatic linguistic indexing of pictures by a statistical modeling approach, IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (2003), 1075–1088.

32.

Jegou ,

Douze and

Schmid , Hamming embedding and weak geometric consistency for large scale image search, in European Conference on Computer Vision, 2008, pp. 304–317.

33.

Kumar ,

Chhabra and

N.K.

Garg , An efficient content based image retrieval system using BayesNet and K-NN, Multimedia Tools and Applications 77(16) (2018), 21557–21570.

34.

Zeng ,

Huang ,

Wang and

Kang , Image retrieval using spatiograms of colors quantized by Gaussian Mixture Models, Neurocomputing 171 (2016), 673–684.

35.

M.E.

ElAlami , A new matching strategy for content based image retrieval system, Applied Soft Computing 14 (2014), 407–418.

36.

Walia and

Pal , Fusion framework for effective color image retrieval, Journal of Visual Communication and Image Representation 25 (2014), 1335–1348.

37.

Q.Y.

Ping Guo and

Zhou , Image semantic analysis, (2015), 569–576.

38.

Li ,

Bhanu and

Dong , Feature synthesized EM algorithm for image retrieval, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 4 (2008), 10.

39.

Wang ,

Cen ,

Zhao ,

Cen ,

Hu ,

Voronin , et al., Separable vocabulary and feature fusion for image retrieval based on sparse representation, Neurocomputing 236 (2017), 14–22.

40.

Douze ,

Jegou ,

Sandhawalia ,

Amsaleg and

Schmid , Evaluation of gist descriptors for web-scale image search, in Proceedings of the ACM International Conference on Image and Video Retrieval, 2009, p. 19.

Effect of complementary visual words versus complementary features on clustering for effective content-based image search

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