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Color space quantization-based clustering for image retrieval

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Abstract

Color descriptors of an image are the most widely used visual features in content-based image retrieval systems. In this study, we present a novel color-based image retrieval framework by integrating color space quantization and feature coding. Although color features have advantages such as robustness and simple extraction, direct processing of the abundant amount of color information in an RGB image is a challenging task. To overcome this problem, a color space clustering quantization algorithm is proposed to obtain the clustering color space (CCS) by clustering the CIE1976L*a*b* space into 256 distinct colors, which adequately accommodate human visual perception. In addition, a new feature coding method called feature-to-character coding (FCC) is proposed to encode the block-based main color features into character codes. In this method, images are represented by character codes that contribute to efficiently building an inverted index by using color features and by utilizing text-based search engines. Benefiting from its high-efficiency computation, the proposed framework can also be applied to large-scale web image retrieval. The experimental results demonstrate that the proposed system can produce a significant augmentation in performance when compared to blockbased main color image retrieval systems that utilize the traditional HSV(Hue, Saturation, Value) quantization method.

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References

  1. Datta R, Joshi D, Li J, Wang J Z. Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys, 2008, 30(2): 5

    Google Scholar 

  2. Liu Y, Zhang D S, Lu G J, Ma W Y. A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 2007, 40(1): 262–282

    Article  MATH  Google Scholar 

  3. Smeulders A W M, Worring M, Santini S, Gupta A, Jain R. Contentbased image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12): 1349–1380

    Article  Google Scholar 

  4. Priya R, Shanmugam T N. A comprehensive review of significant researches on content based indexing and retrieval of visual information. Frontiers of Computer Science, 2013, 7(5): 782–799

    Article  MathSciNet  Google Scholar 

  5. Bian W, Tao D C. Biased Discriminant Euclidean Embedding for Content-Based Image Retrieval. IEEE Transactions on Image Processing, 2010, 19(2): 545–554

    Article  MathSciNet  MATH  Google Scholar 

  6. Kato T. Database architecture for content-based image retrieval. Proceedings of SPIE: The International Society for Optical Engineering, 1992, 1662: 112–123

    Article  Google Scholar 

  7. Tak Y S, Hwang E. Tertiary hash tree: indexing structure for contentbased image retrieval. In: Proceedings of the 20th International Conference on Pattern Recognition. 2010

    Google Scholar 

  8. Liu Z, Li H Q, Zhang L Y, Zhou WG, Tian Q. Cross-indexing of binary SIFT codes for large-scale image search. IEEE Transactions on Image Processing, 2014, 23(5): 2047–2057

    Article  MathSciNet  MATH  Google Scholar 

  9. Kong G P, Dong L, Dong W P, Zheng L, Tian Q. Coarse2Fine: twolayer fusion for image retrieval. 2016, arXiv:1607.00719

    Google Scholar 

  10. Chang R, Xiao Z M, Wong K S, Qi X J. Learning a weighted semantic manifold for content-based image retrieval. In: Proceedings of the 19th IEEE International Conference on Image Processing. 2012

    Google Scholar 

  11. Zhao F, Huang Y Z, Wang L, Tan T N. Deep semantic ranking based hashing for multi-label image retrieval. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 1556–1564

    Google Scholar 

  12. Ma H, Zhu J K, Lyu M R T, King I. Bridging the semantic gap between image contents and tags. IEEE Transactions on Multimedia, 2010, 12(5): 462–473

    Article  Google Scholar 

  13. Barrett S, Chang R, Qi X J. A fuzzy combined learning approach to content-based image retrieval. In: Proceedings of IEEE International Conference on Multimedia and Expo. 2009, 838–841

    Google Scholar 

  14. Liang Y, Dong L, Xie S S, Lv N, Xu Z Y. Compact feature based clustering for large-scale image retrieval. In: Proceedings of IEEE International Conference on Multimedia and Expo Workshops. 2014, 1–6

    Google Scholar 

  15. Chen WT, Liu WC, Chen MS. Adaptive color feature extraction based on image color distributions. IEEE Transactions on Image Processing, 2010, 19(8): 2005–2016

    Article  MathSciNet  MATH  Google Scholar 

  16. Xiao Z M, Qi X J. Block-based long-term content-based image retrieval using multiple features. In: Proceedings of IEEE International Conference on Multimedia and Expo. 2011

    Google Scholar 

  17. Xie B J, Liu Y, Zhang H, Yu J. Efficient image representation for object recognition via pivots selection. Frontiers of Computer Science, 2015, 9(2): 383–391

    Article  Google Scholar 

  18. Chang R, Qi X J. A hierarchical manifold subgraph ranking system for content-based image retrieval. In: Proceedings of IEEE International Conference on Multimedia and Expo. 2013

    Google Scholar 

  19. Chai L S, Qin Z, Zhang H G, Guo J, Shelton C R. Re-ranking using compression-based distance measure for content-based commercial product image retrieval. In: Proceedings of the 19th IEEE International Conference on Image Processing. 2012, 1941–1944

    Google Scholar 

  20. Xu C, Li Y X, Zhou C, Xu C. Learning to rerank images with enhanced spatial verification. In: Proceedings of IEEE International Conference on Image Processing. 2012, 1933–1936

    Google Scholar 

  21. Zhang L N, Shum H P H, Shao L. Discriminative Semantic Subspace Analysis for Relevance Feedback. IEEE Transactions on Image Processing, 2016, 25(3): 1275–1287

    MathSciNet  Google Scholar 

  22. Lin X F, Gokturk B, Sumengen B, Diem V. Visual search engine for product images. In: Proceedings of SPIE, Multimedia Content Access: Algorithms and Systems II. 2008

    Google Scholar 

  23. Xu WG, Zhang Y F, Lu J J, Li R, Xie Z H. A framework ofWeb image search engine. In: Proceedings of IEEE International Joint Conference on Artificial Intelligence. 2009, 522–525

    Google Scholar 

  24. Jiang F, Hu H M, Zheng J. A hierarchal BoW for image retrieval by enhancing feature salience. Neurocomputing, 2016, 175: 146–154

    Article  Google Scholar 

  25. Michael J S, Dana H B. Color indexing. International Journal of Computer Vision, 1991, 7(1): 11–32

    Article  Google Scholar 

  26. Ke V D S, Gevers T, Snoek C G. Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1582–1596

    Article  Google Scholar 

  27. Luo X Y, Zhang J, Dai Q H. Hybrid fusion and interpolation algorithm with near-infrared image. Frontiers of Computer Science, 2015, 9(3): 375–382

    Article  Google Scholar 

  28. Zhang Y G, Gao L J, Gao W, Liu J. Combining color quantization with curvelet transform for image retrieval. In: Proceedings of International Conference on Artificial Intelligence and Computational Intelligence. 2010, 474–479

    Google Scholar 

  29. Pun C M, Wong C F. Image retrieval using a novel color quantization approach. In: Proceedings of the 9th IEEE International Conference on Signal Processing. 2008, 773–776

    Google Scholar 

  30. Zhang H, Hu R M, Chang J, Leng Q M, Chen Y. Research of image retrieval algorithms based on color. In: Proceedings of International Conference on Artificial Intelligence and Computational Intelligence. 2011, 516–522

    Chapter  Google Scholar 

  31. Zheng L, Wang S J, Liu Z Q, Tian Q. Packing and padding: coupled multi-index for accurate image retrieval. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2014, 1947–1954

    Google Scholar 

  32. Jégou H, Douze M, Schmid C. Hamming embedding and weak geometric consistency for large scale image search. In: Proceedings of European Conference on Computer Vision. 2008, 304–317

    Google Scholar 

  33. Dong L, Liang Y, Kong G, Zhang Q N, Cao X C, Izquierdo E. Holons visual representation for image retrieval. IEEE Transactions on Multimedia, 2016, 18(4): 714–725

    Article  Google Scholar 

  34. Jégou H, Douze M, Schmid C. Improving bag-of-features for large scale image search. International Journal of Computer Vision, 2010, 87(3): 316–336

    Article  Google Scholar 

  35. Arandjelović R, Zisserman A.. Three things everyone should know to improve object retrieval. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2012, 2911–2918

    Google Scholar 

  36. Chen Y, Hao P W. Optimal transform in perceptually uniform color space and its application in image retrieval. In: Proceedings of the 7th IEEE International Conference on Signal Processing. 2004, 1107–1110

    Google Scholar 

  37. McQueen J. Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. 1967, 281–297

    Google Scholar 

  38. Chen TW, Chen Y L, Chen S Y. Fast image segmentation based on KMeans clustering with histograms in HSV color space. In: Proceedings of the 10th IEEE Workshop on Multimedia Signal Processing. 2008, 322–325

    Google Scholar 

  39. Jin S. The design and research of personalized search engine based on Solr. Dissertation for the Master Degree. Beijing: Beijing University of Chemical Technology, 2011

    Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61370149), in part by the Fundamental Research Funds for the Central Universities (ZYGX2013J083), and in part by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.

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Correspondence to Le Dong.

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Le Dong received the PhD degree in electronic engineering and computer science from Queen Mary, University of London, UK in 2009. She is an associate professor in University of Electronic Science and Technology of China, China. Her research interests include computer vision, big data analysis, and biologically inspired system.

Wenpu Dong is an undergraduate majoring in computer science and technology at University of Electronic Science and Technology of China, China. His research interest is mainly on image retrieval.

Ning Feng is a PhD student majoring in computer science and technology at University of Electronic Science and Technology of China, China. His research interests are computer vision and image segmentation.

Mengdie Mao is an undergraduate majoring in computer science and technology at University of Electronic Science and Technology of China, China. Her research interest is mainly on image retrieval and deep learning.

Long Chen received his master degree in University of Electronic Science and Technology of China, China in 2013. He is now working at Chengdu FunMi Technology Company. His research interests are computer vision and image retrieval.

Gaipeng Kong is an undergraduate majoring in computer science and technology at University of Electronic Science and Technology of China, China. Her research interest is mainly on image retrieval.

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Dong, L., Dong, W., Feng, N. et al. Color space quantization-based clustering for image retrieval. Front. Comput. Sci. 11, 1023–1035 (2017). https://doi.org/10.1007/s11704-016-5538-y

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