We present here the results for a comparative study of
some techniques, available in the literature, related to the relevance
feedback mechanism in the case of a short-term learning. Only one
method among those considered here is belonging to the data mining
field which is the K-nearest neighbors algorithm (KNN) while the
rest of the methods is related purely to the information retrieval field
and they fall under the purview of the following three major axes:
Shifting query, Feature Weighting and the optimization of the
parameters of similarity metric. As a contribution, and in addition to
the comparative purpose, we propose a new version of the KNN
algorithm referred to as an incremental KNN which is distinct from
the original version in the sense that besides the influence of the
seeds, the rate of the actual target image is influenced also by the
images already rated. The results presented here have been obtained
after experiments conducted on the Wang database for one iteration
and utilizing color moments on the RGB space. This compact
descriptor, Color Moments, is adequate for the efficiency purposes
needed in the case of interactive systems. The results obtained allow
us to claim that the proposed algorithm proves good results; it even
outperforms a wide range of techniques available in the literature.
[1] Qiu, G. P. (2004). Embedded color image coding for content based
retrieval. Journal of Visual Communication and Image Representation,
15(4), 507-521.W.-K. Chen, Linear Networks and Systems (Book style).
Belmont, CA: Wadsworth, 1993, pp. 123–135.
[2] M. Swain and D. Ballard « Color Indexing » International Journal of
Computer Vision, vol. 7, pp. 11-32, 1991.B. Smith, "An approach to
graphs of linear forms (Unpublished work style),” unpublished.
[3] Gong Y., Chuan C. H., Xiaoyi G. «Image Indexing and Retrieval Using
Color Histograms», Multimedia Tools and Applications, vol. 2, pp. 133-
156, 1996.
[4] G. Pass, R. Zabith, Histogramme Refinement for Content based Image
retrieval. IEEE Workshop on Applications of Computer Vision pp. 96-
102, 1996.
[5] Similarity of color images. Stricker, M. et Orengo, M. 1995. Storage and
Retrieval for Image and Video Database III.
[6] Ling Guan, Yongjin Wang, Rui Zhang, Yun Tie, Adrian Bulzacki and
Muhammad Talal Ibrahim, « Multimodal Information Fusion for
Selected Multimedia Applications ». Int. J. Multimedia Intelligence and
Security, Vol. 1, No. 1, 2010.
[7] Zhiyong Cheng, Jing Ren, Jialie Shen and Haiyan Miao, «The Effects of
Heterogenous Information Combination on Large Scale Social Image
Search». ICIMCS’11, August 5-7, 2011, Chengdu, Sichuan, China.
[8] Xiangyu Jin and James C. French, « Improving Image Retrieval
Evectiveness via Multiple Queries ». MMDB’03, November 7, 2003,
New Orleans, Louisiana, USA.
[9] Sean D. MacArthur, Carla E. Brodley, Avinash C. Kak and Lynn S.
Broderick. « Interactive Content based Image Retrieval Using Relevance
Feedback ». Computer Vision and Image Understanding 88, 55-75
(2002). Doi : 10.1006/cviu.2002.0977.
[10] Y. Rui, T.S. Huang, M. Ortega, S. Mehrotra, Relevance feedback: a
power tool for interactive content-based image retrieval, IEEE Trans.
Circuits Syst. Video Technol. 8 (5) (1998) 644–655
[11] A. Kushki, P. Androutsos, K.N. Plataniotis, A.N. Venetsanopoulos,
Query feedback for interactive image retrieval, IEEE Trans. Circuits
Syst. Video Technol. 14 (5) (2004) 644–655.
[12] Xiang Sean Zhou, Thomas S. Huang, «Relevance Feedback in Contentbased
Image Retrieval: Some Recent Advances», Journal of Information
Sciences 148 (2002) 129-137.
[13] G. Salton, Automatic Text Processing, Addison-Wesley, Reading, Mass,
1989.
[14] Salton, G., McGill, MJ. Introduction to modern information retrieval.
McGraw-Hill, New York, 1998
[15] Kriengkrai Porkaew, Kaushik Chakrabarti and Sharad Mehrotra. «Query
Refinement for Multimedia Similarity Retrieval in MARS».
Multimedia’99 Proceedings of the seventh ACM international
conference on multimedia (Part 1), pp. 235-238. doi:
10.1145/319463.319613.
[16] Y. Rui, T. S. Huang, and S. Mehrotra, « Content based Image Retrieval
with Relevance Feedback: in MARS, Proc. IEEE intern.1 conf. On
Image Processing, Santa Barbara, CA, 1997, pp. 815-818.
[17] Giorgio Giacinto, Fabio Roli and Giorgio Fumera «Adaptive Query
Shifting For Content based Image Retrieval». P. Perner (Ed.): MLDM
2001, LNAI 2123, pp. 337-346, 2001.
[18] M. L. Kherfi, D. Ziou, Relevance Feedback for CBIR: A new approach
based on probabilistic feature weighting with positive and negative
examples, IEEE Transactions on Image Processing 15 (2006) 1017-
1030.
[19] Y. Ishikawa, R. Subramanys, C. Faloutsos, MindReader: querying
databases through multiple examples, Proceedings of the 24th VLDB
Conference, 1998, pp. 433–438.
[20] Y. Rui, T.S. Huang, Relevance feedback techniques in image retrieval,
in: M.S. Lew (Ed.), Principles of Visual Information Retrieval, Springer,
London, 2001, pp. 219–258.
[21] A. K. Jain, M. N. Murty and P. J. Flunn. Data Clustering: A Review.
2000 ACM 0360-0300/99/0900-0001.
[22] V.S.V.S Murthy, E. Vamsidhar, J.N.V.R. Swarup Kumar and P. Sankara
Rao. : Content Based Image Retrieval using Hieararchical and K-means
Clustering Techniques. International Journal of Engineering Science and
Technology Vol. 2(3), 2010, 209-212.
[23] R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis.
New York: Wiley, 1973.
[24] X. S. Zhou and T. S. Huang, Small sample learning during multimedia
retrieval using BiasMap, in Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, 2001.
[25] Yossi Rubner, Carlo Tomasi and Leonidas J. Guibas. The Earth Mover’s
Distance as a Metric for Image Retrieval. International Journal of
Computer Vision 40(2), 99-121, (2000).
[26] T. Dharani, I. Laurence Aroquiaraj. Content Based Image Retrieval
System using Feature Classification with Modified KNN Algorithm.
arXiv preprint arXiv:1307.4717.
[27] Pierre-Alain Moellic, Jean-Emmanuel Haugeard and Guillaume Pittel.
Image Clustering Based on a Shared Nearest Neighbors Approach for
Tagged Collections. CIVR’08 Proceedings of the 2008 International
Conference on Content-based Image and Video Retrieval.pages: 269-
278.
[28] Giorgio Giacinto, A Nearest-Neighbor Approach to Relevance Feedback
in Content Based Image Retrieval. CIVR’07, July 9-11, 2007,
Amsterdam, The Netherlands.
[29] Ray-I Chang, Shu- Yu Lin, Jan-Ming Ho, Chi-Wen Fann, and Yu-Chun
Wang. : A Novel Content Based Image Retrieval System using Kmeans/
KNN with Feature Extraction. ComSIS Vol. 9, No. 4, Special
Issue, December 2012.
[30] http://Wang.ist.psu.edu/docs/related.shtml
[31] Babu, G. P., B. M. Mehre and M. S. Kanhalli, 1995. Color Indexing for
Efficient Image Retrieval. Multimedia Tools Application. 1: 327-348.
DOI: 10.1007/BF01215882.
[1] Qiu, G. P. (2004). Embedded color image coding for content based
retrieval. Journal of Visual Communication and Image Representation,
15(4), 507-521.W.-K. Chen, Linear Networks and Systems (Book style).
Belmont, CA: Wadsworth, 1993, pp. 123–135.
[2] M. Swain and D. Ballard « Color Indexing » International Journal of
Computer Vision, vol. 7, pp. 11-32, 1991.B. Smith, "An approach to
graphs of linear forms (Unpublished work style),” unpublished.
[3] Gong Y., Chuan C. H., Xiaoyi G. «Image Indexing and Retrieval Using
Color Histograms», Multimedia Tools and Applications, vol. 2, pp. 133-
156, 1996.
[4] G. Pass, R. Zabith, Histogramme Refinement for Content based Image
retrieval. IEEE Workshop on Applications of Computer Vision pp. 96-
102, 1996.
[5] Similarity of color images. Stricker, M. et Orengo, M. 1995. Storage and
Retrieval for Image and Video Database III.
[6] Ling Guan, Yongjin Wang, Rui Zhang, Yun Tie, Adrian Bulzacki and
Muhammad Talal Ibrahim, « Multimodal Information Fusion for
Selected Multimedia Applications ». Int. J. Multimedia Intelligence and
Security, Vol. 1, No. 1, 2010.
[7] Zhiyong Cheng, Jing Ren, Jialie Shen and Haiyan Miao, «The Effects of
Heterogenous Information Combination on Large Scale Social Image
Search». ICIMCS’11, August 5-7, 2011, Chengdu, Sichuan, China.
[8] Xiangyu Jin and James C. French, « Improving Image Retrieval
Evectiveness via Multiple Queries ». MMDB’03, November 7, 2003,
New Orleans, Louisiana, USA.
[9] Sean D. MacArthur, Carla E. Brodley, Avinash C. Kak and Lynn S.
Broderick. « Interactive Content based Image Retrieval Using Relevance
Feedback ». Computer Vision and Image Understanding 88, 55-75
(2002). Doi : 10.1006/cviu.2002.0977.
[10] Y. Rui, T.S. Huang, M. Ortega, S. Mehrotra, Relevance feedback: a
power tool for interactive content-based image retrieval, IEEE Trans.
Circuits Syst. Video Technol. 8 (5) (1998) 644–655
[11] A. Kushki, P. Androutsos, K.N. Plataniotis, A.N. Venetsanopoulos,
Query feedback for interactive image retrieval, IEEE Trans. Circuits
Syst. Video Technol. 14 (5) (2004) 644–655.
[12] Xiang Sean Zhou, Thomas S. Huang, «Relevance Feedback in Contentbased
Image Retrieval: Some Recent Advances», Journal of Information
Sciences 148 (2002) 129-137.
[13] G. Salton, Automatic Text Processing, Addison-Wesley, Reading, Mass,
1989.
[14] Salton, G., McGill, MJ. Introduction to modern information retrieval.
McGraw-Hill, New York, 1998
[15] Kriengkrai Porkaew, Kaushik Chakrabarti and Sharad Mehrotra. «Query
Refinement for Multimedia Similarity Retrieval in MARS».
Multimedia’99 Proceedings of the seventh ACM international
conference on multimedia (Part 1), pp. 235-238. doi:
10.1145/319463.319613.
[16] Y. Rui, T. S. Huang, and S. Mehrotra, « Content based Image Retrieval
with Relevance Feedback: in MARS, Proc. IEEE intern.1 conf. On
Image Processing, Santa Barbara, CA, 1997, pp. 815-818.
[17] Giorgio Giacinto, Fabio Roli and Giorgio Fumera «Adaptive Query
Shifting For Content based Image Retrieval». P. Perner (Ed.): MLDM
2001, LNAI 2123, pp. 337-346, 2001.
[18] M. L. Kherfi, D. Ziou, Relevance Feedback for CBIR: A new approach
based on probabilistic feature weighting with positive and negative
examples, IEEE Transactions on Image Processing 15 (2006) 1017-
1030.
[19] Y. Ishikawa, R. Subramanys, C. Faloutsos, MindReader: querying
databases through multiple examples, Proceedings of the 24th VLDB
Conference, 1998, pp. 433–438.
[20] Y. Rui, T.S. Huang, Relevance feedback techniques in image retrieval,
in: M.S. Lew (Ed.), Principles of Visual Information Retrieval, Springer,
London, 2001, pp. 219–258.
[21] A. K. Jain, M. N. Murty and P. J. Flunn. Data Clustering: A Review.
2000 ACM 0360-0300/99/0900-0001.
[22] V.S.V.S Murthy, E. Vamsidhar, J.N.V.R. Swarup Kumar and P. Sankara
Rao. : Content Based Image Retrieval using Hieararchical and K-means
Clustering Techniques. International Journal of Engineering Science and
Technology Vol. 2(3), 2010, 209-212.
[23] R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis.
New York: Wiley, 1973.
[24] X. S. Zhou and T. S. Huang, Small sample learning during multimedia
retrieval using BiasMap, in Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, 2001.
[25] Yossi Rubner, Carlo Tomasi and Leonidas J. Guibas. The Earth Mover’s
Distance as a Metric for Image Retrieval. International Journal of
Computer Vision 40(2), 99-121, (2000).
[26] T. Dharani, I. Laurence Aroquiaraj. Content Based Image Retrieval
System using Feature Classification with Modified KNN Algorithm.
arXiv preprint arXiv:1307.4717.
[27] Pierre-Alain Moellic, Jean-Emmanuel Haugeard and Guillaume Pittel.
Image Clustering Based on a Shared Nearest Neighbors Approach for
Tagged Collections. CIVR’08 Proceedings of the 2008 International
Conference on Content-based Image and Video Retrieval.pages: 269-
278.
[28] Giorgio Giacinto, A Nearest-Neighbor Approach to Relevance Feedback
in Content Based Image Retrieval. CIVR’07, July 9-11, 2007,
Amsterdam, The Netherlands.
[29] Ray-I Chang, Shu- Yu Lin, Jan-Ming Ho, Chi-Wen Fann, and Yu-Chun
Wang. : A Novel Content Based Image Retrieval System using Kmeans/
KNN with Feature Extraction. ComSIS Vol. 9, No. 4, Special
Issue, December 2012.
[30] http://Wang.ist.psu.edu/docs/related.shtml
[31] Babu, G. P., B. M. Mehre and M. S. Kanhalli, 1995. Color Indexing for
Efficient Image Retrieval. Multimedia Tools Application. 1: 327-348.
DOI: 10.1007/BF01215882.
@article{"International Journal of Information, Control and Computer Sciences:59804", author = "Mawloud Mosbah and Bachir Boucheham", title = "Relevance Feedback within CBIR Systems", abstract = "We present here the results for a comparative study of
some techniques, available in the literature, related to the relevance
feedback mechanism in the case of a short-term learning. Only one
method among those considered here is belonging to the data mining
field which is the K-nearest neighbors algorithm (KNN) while the
rest of the methods is related purely to the information retrieval field
and they fall under the purview of the following three major axes:
Shifting query, Feature Weighting and the optimization of the
parameters of similarity metric. As a contribution, and in addition to
the comparative purpose, we propose a new version of the KNN
algorithm referred to as an incremental KNN which is distinct from
the original version in the sense that besides the influence of the
seeds, the rate of the actual target image is influenced also by the
images already rated. The results presented here have been obtained
after experiments conducted on the Wang database for one iteration
and utilizing color moments on the RGB space. This compact
descriptor, Color Moments, is adequate for the efficiency purposes
needed in the case of interactive systems. The results obtained allow
us to claim that the proposed algorithm proves good results; it even
outperforms a wide range of techniques available in the literature.
", keywords = "CBIR, Category Search, Relevance Feedback (RFB),
Query Point Movement, Standard Rocchio’s Formula, Adaptive
Shifting Query, Feature Weighting, Optimization of the Parameters
of Similarity Metric, Original KNN, Incremental KNN.", volume = "8", number = "4", pages = "589-5", }
