A graph-based relevance feedback mechanism in content-based image retrieval

Knowledge-Based Systems

Volumn 73, Issue 1, 2015, Pages 254-264

  Kundu, Malay Kumar ^a   Chowdhury, Manish ^a   Bulo, Samuel Rota ^b  

^a INDIAN STATISTICAL INSTITUTE   (India)

^b ITC IRST   (Italy)

Author keywords

Content based image retrieval; Feature evaluation index; NSCT; Re ranking; Relevance feedback

Indexed keywords

CONTENT BASED RETRIEVAL; GRAPH THEORY; GRAPHIC METHODS; IMAGE ANALYSIS; IMAGE ENHANCEMENT; INFORMATION MANAGEMENT; MATHEMATICAL TRANSFORMATIONS; SEMANTICS;

CONTENT BASED IMAGE RETRIEVAL; FEATURE EVALUATION; NSCT; RE-RANKING; RELEVANCE FEEDBACK;

SEARCH ENGINES;

EID: 84922783718     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2014.10.009     Document Type: Article

References (62)

1. A. Dong, B. Bhanu, Active concept learning in image databases, IEEE Trans. Syst. Man Cybern. B, Cybern. 35 (3) (2005) 450-456.
2. A. Adler, M.E. Schuckers, Comparing human and automatic face recognition performance, IEEE Trans. Syst. Man Cybern. B, Cybern. 37 (5) (2007) 1248-1255.
3. A. Perina, M. Cristani, V. Murino, Unsupervised learning of saliency concepts for natural image classification and retrieval, in: Progress in Pattern Recognition, Image Analysis and Applications, 13th Iberoamerican Congress on Pattern Recognition, CIARP, Lecture Notes in Computer Science, vol. 5197, Springer, Berlin, Heidelberg, 2008, pp. 169-177.
4. S. Zhang, J. Huang, H. Li, D.N. Metaxas, Automatic image annotation and retrieval using group sparsity, IEEE Trans. Syst. Man Cybern. B, Cybern. 42 (3) (2012) 838-849.
5. A. Bishnu, B.B. Bhattacharya, M.K. Kundu, C.A. Murthy, T. Acharya, Euler vector for search and retrieval of gray-tone images, IEEE Trans. Syst. Man Cybern. B, Cybern. 35 (4) (2005) 801-812.
6. M.E. ElAlami, Supporting image retrieval framework with rule base system, Knowl.-Based Syst. 24 (2011) 331-340.
7. L. Zhang, L. Wang, W. Lin, Generalized biased discriminant analysis for content-based image retrieval, IEEE Trans. Syst. Man Cybern. B, Cybern. 42 (1) (2012) 282-290.
8. J. Yu, D. Liu, D. Tao, H.S. Seah, On combining multiple features for cartoon character retrieval and clip synthesis, IEEE Trans. Syst. Man Cybern. B, Cybern. 42 (5) (2012) 1413-1427.
9. R. Datta, D. Joshi, J. Li, J.Z. Wang, Image retrieval: ideas, influences, and trends of the new age, ACM Comput. Surv. 40 (2) (2008) 1-60.
10. G. Quellec, M. Lamard, G. Cazuguel, B. Cochener, C. Roux, Adaptive nonseparable wavelet transform via lifting and its application to content-based image retrieval, IEEE Trans. Image Process. 19 (1) (2010) 25-35.
11. E. Yildizer, A.M. Balci, T.N. Jarada, R. Alhajj, Integrating wavelets with clustering and indexing for effective content-based image retrieval, Knowl.-Based Syst. 31 (2012) 55-66.
12. Q. Tian, N. Sebe, M.S. Lew, E. Loupias, T. Huang, Image retrieval using wavelet-based salient points, J. Electron. Imag. 10 (4) (2001) 835-849.
13. M. Kokare, P.K. Biswas, B.N. Chatterji, Rotation-invariant texture image retrieval using rotated complex wavelet filters, IEEE Trans. Syst. Man Cybern. B, Cybern. 36 (6) (2006) 1273-1282.
14. T. Celik, T. Tjahjadi, Multiscale texture classification and retrieval based on magnitude and phase features of complex wavelet subbands, Comput. Electr. Eng. 37 (5) (2011) 729-743.
15. M. Banerjee, M.K. Kundu, Edge based features for content based image retrieval, Pattern Recogn. 36 (11) (2003) 2649-2661.
16. M. Acharyya, M.K. Kundu, Extraction of features using M-band wavelet packet frames and their neuro-fuzzy evaluation for multi-texture segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 25 (12) (2003) 1639-1644.
17. M.N. Do, M. Vetterli, The contourlet transform: an efficient directional multiresolution image representation, IEEE Trans. Image Process. 14 (12) (2005) 2091-2106.
18. S.M. Youssef, ICTEDCT-CBIR: integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval, Comput. Electr. Eng. 38 (5) (2012) 1358-1376.
19. M.K. Kundu, M. Chowdhury, M. Banerjee, Interactive image retrieval with wavelet features, in: Proc. of 4th Int. Conf. on Pattern recognition and Machine Intelligence, 2011, pp. 162-172.
20. A.L. da Cunha, J. Zhou, M.N. Do, The nonsubsampled contourlet transform: theory, design, and applications, IEEE Trans. Image Process. 15 (10) (2006) 3089-3101.
21. Q. Zhang, L. Gao, Medical image retrieval based on nonsubsampled contourlet transform and fractal dimension, in: Proc. of 3rd Int. Conf. on Bioinformatics and Biomedical Engineering, 2009, pp. 1-4.
22. X. Xu, D. Zhang, X. Zhang, An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine, Opt. Appl. XXXIX (3) (2009) 601-615.
23. H. Ng-Duc, T. Le-Tien, T. Do-Honl, C. Bui-Thu, T. Ng-Xuan, Image retrieval using contourlet based interest points, in: Proc. of 10th Int. Conf. on Inf. Sci. Signal Process. and their Applications, 2010, pp. 93-96.
24. M. Chowdhury, S. Das, M.K. Kundu, Compact image signature generation: an application in image retrieval, in: Proc. 5th Int. Conf. on Computer Science and Inf. Technology, 2013, pp. 1-7.
25. L. Xu, I. King, A pca approach for fast retrieval of structural patterns in attributed graphs, IEEE Trans. Syst. Man Cybern. B, Cybern. 31 (5) (2001) 812-817.
26. T. Xia, D. Tao, T. Mei, Y. Zhang, Multiview spectral embedding, IEEE Trans. Syst. Man Cybern. B, Cybern. 40 (6) (2010) 1438-1446.
27. D.T.B. Xie, Yang Mu, K. Huang, m-SNE: multiview stochastic neighbor embedding, IEEE Trans. Syst. Man Cybern. B, Cybern. 41 (4) (2011) 1088-1096.
28. P. Mitra, C.A. Murthy, S.K. Pal, Unsupervised feature selection using feature similarity, IEEE Trans. Pattern Anal. Mach. Intell. 24 (4) (2002) 301-312.
29. T.W.S. Chow, P. Wang, E.W.M. Ma, A new feature selection scheme using a data distribution factor for unsupervised nominal data, IEEE Trans. Syst. Man Cybern. B, Cybern. 38 (2) (2008) 499-509.
30. K.Z. Mao, Identifying critical variables of principal components for unsupervised feature selection, IEEE Trans. Syst. Man Cybern. B, Cybern. 35 (2) (2005) 339-344.
31. Q. Huang, D. Tao, X. Li, L. Jin, G. Wei, Exploiting local coherent patterns for unsupervised feature ranking, IEEE Trans. Syst. Man Cybern. B, Cybern. 41 (6) (2011) 1471-1482.
32. E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, A simultaneous feature adaptation and feature selection method for content-based image retrieval systems, Knowl.-Based Syst. 39 (2013) 85-94.
33. M. Banerjee, M.K. Kundu, P. Maji, Content-based image retrieval using visually significant point features, Fuzzy Set. Syst. 160 (23) (2009) 3323-3341.
34. L. Wang, K.L. Chan, P. Xue, A criterion for optimizing kernel parameters in kbda for image retrieval, IEEE Trans. Syst. Man Cybern. B, Cybern. 35 (3) (2005) 556-562.
35. E. Cheng, F. Jing, L. Zhang, A unified relevance feedback framework for web image retrieval, IEEE Trans. Image Process. 18 (6) (2009) 1350-1357.
36. P.Y. Yin, B. Bhanu, K.C. Chang, A. Dong, Integrating relevance feedback techniques for image retrieval using reinforcement learning, IEEE Trans. Pattern Anal. Mach. Intell. 27 (10) (2005) 1536-1551.
37. J. Han, K.N. Ngan, M. Lin, H.J. Zhang, A memory learning framework for effective image retrieval, IEEE Trans. Image Process. 14 (4) (2005) 521-524.
38. F.C. Chang, H.M. Hang, A relevance feedback image retrieval scheme using multi-instance and pseudo image concepts, IEICE Trans. Inf. Syst. 89D (5) (2006) 1720-1731.
39. J. He, M. Li, H. Zhang, H. Tong, C. Zhang, Manifold ranking based image retrieval, in: IEEE Int. Conf. on Mult. and Expo, 2004, pp. 9-16.
40. K. Tieu, P. Viola, Boosting image retrieval, in: IEEE Conf. Computer Vision and Patt. Recogn. 1, 2000, pp. 228-235.
41. L. Zhang, F. Lin, B. Zhang, Support vector machine learning for image retrieval, in: Int. Conf. Image Process., 2001, pp. 721-724.
42. S. Rota Bulò, M. Rabbi, M. Pelillo, Content-based image retrieval with relevance feedback using random walks, Pattern Recogn. 44 (9) (2011) 2109-2122.
43. H. Sahbi, P. Etyngier, J.Y. Audibert, R. Keriven, Manifold learning using robust graph laplacian for interactive image search, in: IEEE Conf. Computer Vision and Patt. Recogn., 2008, pp. 1-8.
44. J. He, H. Tong, M. Li, W.Y. Ma, C. Zhang, Multiple random walk and its application in content-based image retrieval, in: Proc. of the 7th ACM SIGMM Int. Workshop on Mult. Inf. Retrieval, ACM, New York, NY, USA, 2005, pp. 151-158.
45. Y. Wu, Q. Tian, T. Huang, Discriminant-em algorithm with application to image retrieval, in: IEEE Conf. Computer Vision and Patt. Recogn. 1, 2000, pp. 222-227.
46. M.J. Huiskes, M.S. Lew, Performance evaluation of relevance feedback methods, in: Proc. of the Int. Conf. on Content-based Image and Video Retrieval, ACM, New York, NY, USA, 2008, pp. 239-248.
47. S. Li, B. Yang, J. Hu, Performance comparison of different multi-resolution transforms for image fusion, Inf. Fusion 12 (2) (2011) 74-84.
48. A.M. Garcia-Perez, The perceived image: efficient modelling of visual inhomogeneity, Spatial Vis. 6 (1992) 89-99.
49. F. Lpez, J. Valiente, R. Baldrich, M. Vanrell, Fast surface grading using color statistics in the cie lab space, in: Pattern Recognition and Image Analysis, Lecture Notes in Computer Science, vol. 3523, Springer, Berlin, Heidelberg, 2005, pp. 666-673.
50. N. Doghmane, Z. Baarir, N. Terki, A. Ouafi, Study of effect of filters and decomposition level in wavelet image compression, Courrier du Savoir 3 (2003) 41-45.
51. E. Mortensen, W. Barrett, Interactive segmentation with intelligent scissors, Graph. Mod. Image Process. 60 (5) (1998) 349-384.
52. L. Grady, Random walks for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 28 (11) (2006) 1768-1783.
53. J.Z. Wang, J. Li, G. Wiederhold, SIMPLIcity: semantics-sensitive integrated matching for picture libraries, IEEE Trans. Pattern Anal. Mach. Intell. 23 (9) (2001) 947-963.
54. A. Oliva, A. Torralba, Modeling the shape of the scene: a holistic representation of the spatial envelope, Int. J. Comput. Vis. 42 (3) (2001) 145-175.
55. G. Griffin, A. Holub, P. Perona, Caltech 256 object category dataset, Technical Report UCB/CSD-04-1366, California Institute of Technology, 2007.
56. O. Ikeda, Segmentation of faces in video footage using controlled weightson hsv color, in: 13th Scandinavian Conf. on Image Analysis, Lecture Notes in Computer Science, vol. 2749, Springer, Berlin, Heidelberg, 2003, pp. 163-170.
57. C.H. Lee, M.F. Lin, Ego-similarity measurement for relevance feedback, Expert Syst. Appl. 37 (1) (2010) 871-877.
58. Y. Yang, F. Nie, D. Xu, J. Luo, Y. Zhuang, Y. Pan, A multimedia retrieval framework based on semi-supervised ranking and relevance feedback, IEEE Trans. Pattern Anal. Mach. Intell. 34 (4) (2012) 723-742.
59. C.H. Lin, R.T. Chen, Y.K. Chan, A smart content-based image retrieval system based on color and texture feature, J. Image Vis. Comput. 27 (6) (2009) 658-665.
60. M.E. ElAlami, A novel image retrieval model based on the most relevant features, Knowl.-Based Syst. 24 (2011) 23-32.
61. E. Yildizer, A.M. Balci, M. Hassan, R. Alhajj, Efficient content-based image retrieval using multiple support vector machines ensemble, Expert Syst. Appl. 39 (3) (2012) 2385-2396.
62. X.Y. Wang, H.Y. Yang, Y.W. Li, F.Y. Yang, Robust color image retrieval using visual interest point features of significant bit-planes, Digital Signal Process. 23 (4) (2013) 1136-1153.