K-means based histogram using multiresolution feature vectors for color texture database retrieval

详细信息    查看全文

• 作者：Cong Bai (1)
  Jinglin Zhang (2)
  Zhi Liu (3) (4)
  Wan-Lei Zhao (5)
  
  1. College of Computer Science ; Zhejiang University of Technology ; 310023 ; Hangzhou ; China
  2. IETR UMR CNRS 6164 ; INSA de Rennes ; Universit茅 Europ茅enne de Bretagne ; Rennes ; France
  3. School of Communication and Information Engineering ; Shanghai University ; Shanghai ; China
  4. IRISA/INRIA-Rennes ; Rennes ; France
  5. INRIA-Rennes ; Rennes ; France
  
• 关键词：Color texture retrieval ; K ; means ; Discrete wavelet transform (DWT) ; Z ; score normalization
• 刊名：Multimedia Tools and Applications
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：74
• 期：4
• 页码：1469-1488
• 全文大小：2,636 KB
• 参考文献：1. Alajlan N, Kamel M S, Freeman G H (2008) Geometry-based image retrieval in binary image databases. IEEE Trans Pattern Anal Machine Intell 30(6):1003鈥?013 CrossRef
  2. Alvarez S, Salvatella A, Vanrell M, Otazu X (2012) Low-dimensional and comprehensive color texture description. Comp Vision Image Underst 116(1):54鈥?7 CrossRef
  3. Bai C, Zou W, Kpalma K, Ronsin J (2012) Efficient colour texture image retrieval by combination of colour and texture features in wavelet domain. Electron Lett 48(23):1463鈥?465 CrossRef
  4. Burghouts G J, Geusebroek J M (2009) Material-specific adaptation of color invariant features. Pattern Recogn Lett 30(3):306鈥?13 CrossRef
  5. Chen T, Ma KK, Chen LH (1998) Discrete wavelet frame representations of color texture features for image query. In: IEEE second workshop on multimedia signal processing
  6. Chun Y D, Seo S Y, Kim N C (2003) Image retrieval using BDIP and BVLC moments. IEEE Trans Circ Systmes Video Technol 13(9):951鈥?57 CrossRef
  7. Chun YD, Kim NC, Jang IH (2008) Content-based image retrieval using multiresolution color and texture features. IEEE Trans Multimed 10(6):1073 鈥?084 CrossRef
  8. Costantini L, Sit P, Capodiferro L, Neri A (2010) Laguerre gauss analysis for image retrieval based on color texture. In: Proc. SPIE, vol 7535
  9. Datta R, Joshi D, Li J, Wang J Z (2008) Image retrieval: Ideas, influences, and trends of the new age. ACM Comput Surv 40(2):1鈥?0 CrossRef
  10. Do M, Vetterli M (2002) Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance. IEEE Trans Image Process 11(2):146 鈥?58 CrossRef
  11. Google (2012) Google image search http://images.google.com/
  12. Jain A K (2010) Data clustering 50 years beyond k-means. Pattern Recog Lett 31(8):651鈥?66 CrossRef
  13. James SW (2007) A Primer on Wavelets and their scientific Appliactions Second edition. Chapman Hall
  14. Kwitt R, Meerwald P (2012) Salzburg texture image database http://www.wavelab.at/sources/STex/, online;Accessed Sep 2012
  15. Kwitt R, Uhl A (2010) Lightweight probabilistic texture retrieval 19(1):241鈥?53
  16. Kwitt R, Meerwald P, Uhl A (2011) Efficient texture image retrieval using copulas in a bayesian framework. IEEE Trans Image Process 20(7):2063 鈥?077 CrossRef
  17. Lee Y B, Park U, Jain A K, Lee S W (2012) Pill-ID: Matching and retrieval of drug pill images. Pattern Recog Lett 33(7):904鈥?10 CrossRef
  18. Lew M, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: State of the art and challenges. ACM Trans Multimed Comput, Commun Appl 2(1):1鈥?9 CrossRef
  19. Liu G H, Zhang L, Hou Y K, Li Z Y, Yang J Y (2010) Image retrieval based on multi-texton histogram. Pattern Recog 43(7):2380鈥?389 CrossRef
  20. Liu Y, Zhang D, Lu G, Ma W (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recog 40(1):262鈥?82. http://linkinghub.elsevier.com/retrieve/pii/S0031320306002184 CrossRef
  21. Lumini A, Maio D (2000) Haruspex: an image database system for query-by-examples. In: 15th International Conference on Pattern Recognition, vol 4
  22. M盲enp盲盲 T, Pietik盲inen M (2004) Classification with color and texture: jointly or separatelyPattern Recog 37(8):1629鈥?640 CrossRef
  23. MIT (2010) Vistex database of textures, http://vismod.media.mit.edu/vismod/imagery, online; Accessed Dec 2010
  24. M眉lller H, Michoux N, Bandon D, Geissbuhler A (2004) A review of content-based image retrieval systems in medical applications-clinical benefits and future directions. Int J Med Inform 73(1):1鈥?3
  25. Ngo C W, Pong T C, Chin R T (2001) Exploiting image indexing techniques in DCT domain. Pattern Recog 34(9):1841鈥?851 CrossRef
  26. Ojala T, Pietik盲inen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recog 29(1):51鈥?9 CrossRef
  27. Permuter H, Francos J, Jermyn I H (2003) Gaussian mixture models of texture and colour for image database retrieval Proc. IEEE International conference on acoustics, speech, signla processing, vol 3, pp 569鈥?72
  28. Picard R, Kabir T, Liu F (1993) Real-time recognition with the entire brodatz texture database. In: IEEE international conference on computet vision pattern recognition (CVPR)
  29. Poursistani P, Nezamabadi-pour H, Moghadam RA, Saeed M (2011). Image indexing and retrieval in JPEG compressed domain based on vector quantization. Math Comp Model
  30. Qi H, Li K, Shen Y, Qu W (2010) An effective solution for trademark image retrieval by combining shape description and feature matching. Pattern Recog 43(6):2017鈥?027 CrossRef
  31. Rafiee G, Dlay S, Woo W (2010) A review of content-based image retrieval. In: 7th International symposium on communication systems networks and digital signal processing (CSNDSP)
  32. Rui Y, Huang T S, Chang S F (1999) Image retrieval: Current techniques, promising directions, and open issues. J Vis Commun Image Represent 10(1):39鈥?2 CrossRef
  33. Smeulders A, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349 鈥?380 CrossRef
  34. Theoharatos C, Pothos V, Laskaris N, Economou G, Fotopoulos S (2006) Multivariate image similarity in the compressed domain using statistical graph matching. Pattern Recog 39(10):1892鈥?904 CrossRef
  35. Tian Y, Mei L (2003) Image retrieval based on multiple features using wavelet. In: Fifth international conference on computational intelligence and multimedia applications
  36. Vellaikal A, Kuo CC (1996) Joint spatial-spectral indexing for image retrieval. In: Proceedings of International Conference on Image Processing, vol 3,
  37. Verdoolaege G, De Backer S, Scheunders P (2008) Multiscale colour texture retrieval using the geodesic distance between multivariate generalized gaussian models. In: 15th IEEE international conference on image processing
  38. Wang X Y, Zhang B B, Yang H Y (2012) Content-based image retrieval by integrating color and texture features. Multimed Tools Appl:1鈥?5
  39. Wang X Y, Yang H Y, Li D M (2013) A new content-based image retrieval technique using color and texture information. Comput Electr Eng 39(3):746鈥?61 CrossRef
  40. Yu H, Li M, Zhang HJ, Feng J (2002) Color texture moments for content-based image retrieval. In: Proceeding IEEE international conference on image processing (ICIP),
  41. Yue J, Li Z, Liu L, Fu Z (2011) Content-based image retrieval using color and texture fused features. Math Comput Model 54(3):1121鈥?127 CrossRef
  42. Zhong D, Def茅e I (2005) DCT histogram optimization for image database retrieval. Pattern Recogn Lett 26(14):2272鈥?281 CrossRef
  
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

Colorand texture are two important features in content-based image retrieval. It has been shown that using the combination of both could provide better performance. In this paper, a K-means based histogram (KBH) using the combination of color and texture features for image retrieval is proposed. Multiresolution feature vectors representing color and texture features are directly generated from the coefficients of Discrete Wavelet Transform (DWT), and K-means is exploited to partition the vector space with the objective to reduce the number of histogram bins. Thereafter, a fusion of z-score normalized Chi-Square distance between KBHs is employed as the similarity measure. Experiments have been conducted on four natural color texture data sets to examine the sensitivity of KBH to its parameters. The performance of the proposed approach has been compared with state-of-the-art approaches. Results evaluated in terms of Precision-Recall and Average Retrieval Rate (ARR) show that our approach outperforms the referred approaches