Content-based texture image retrieval by histogram of curvelets

Content-based texture image retrieval by histogram of curvelets

: Curvelet decomposition is a multiscale analysis method defined for 2D and 3D signals that can represent curve-like features with great sparsity. A genuine method based on histograms of curvelets is proposed for content-based texture image retrieval. The accuracy of the method is analyzed for rotation invariance, curvelet scale-orientation size, and bin size. The results are given with precision-recall graphs. Experimental results on the Brodatz database show promising results for the proposed method compared to curvelet subband statistical features.

___

  • [1] Do MN, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation. IEEE T Image Process 2005; 14: 2091-2106.
  • [2] Dettori L, Semler L. A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography. Comput Biol Med 2007; 37: 486-498.
  • [3] Howarth P, R¨uger S. Evaluation of texture features for content-based image retrieval. Lect Notes Comput Sc 2004; 3115: 326-334.
  • [4] Xu DH, Kurani AS, Furst JD, Raicu DS. Run-length encoding for volumetric texture. In: The 4th IASTED International Conference on Visualization, Imaging, and Image Processing; 2004.
  • [5] Herv´e N, Boujemaa N. Image annotation: which approach for realistic databases? In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval; 2007; Amsterdam, the Netherlands. pp. 170-177.
  • [6] Ngo CW, Pong TC, Chin RT. Exploiting image indexing techniques in DCT domain. Pattern Recogn 2001; 34: 1841-1851.
  • [7] Mishra AK, Raghav S. Local fractal dimension based ECG arrhythmia classification. Biomed Signal Proces 2010; 5: 114-123.
  • [8] Aptoula E. Comparative study of moment based parameterization for morphological texture description. J Vis Commun Image R 2012; 23: 1213-1224.
  • [9] Datta R, Joshi D, Li J, Wang JZ. Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 2008; 40: 5:1-5:60.
  • [10] Liu Y, Zhang D, Lu G, Ma WY. A survey of content-based image retrieval with high-level semantics. Pattern Recogn 2007; 40: 262-282.
  • [11] Islam MM, Zhang D, Lu G. Region based color image retrieval using curvelet transform. Lect Notes Comput Sc 2010; 5995: 448-457.
  • [12] Cand`es EJ, Donoho DL. Curvelets, multiresolution representation, and scaling laws. Proc SPIE 2000; 4119: 1-12.
  • [13] G´omez F, Romero E. Rotation invariant texture characterization using a curvelet based descriptor. Pattern Recogn Lett 2011; 32: 2178-2186.
  • [14] Uslu E, Albayrak S. Curvelet-based synthetic aperture radar image classification. IEEE Geosci Remote S 2014; 11: 1071-1075.
  • [15] Ma J, Plonka G. The curvelet transform. IEEE Signal Proc Mag 2010; 27: 118-133.
  • [16] Do MN. Contourlets and sparse image expansions. Proc SPIE 2013; 5207: 560-570.
  • [17] Cand`es E, Demanet L, Donoho D, Ying L. Fast Discrete Curvelet Transforms. 2005. Available online at http://www.curvelet.org/papers/FDCT.pdf.
  • [18] Liu H, Song D, R¨uger S, Hu R, Uren V. Comparing dissimilarity measures for content-based image retrieval. In: Proceedings of the 4th Asia Information Retrieval Conference on Information retrieval Technology; 2008; Harbin, China. pp. 44-50.
  • [19] Singha M, Hemachandran K, Paul A. Content-based image retrieval using the combination of the fast wavelet transformation and the colour histogram. IET Image Process 2012; 6: 1221-1226.
  • [20] Brodatz P. Textures: A Photographic Album for Artists and Designers. New York, NY, USA: Dover Publications, 1966.
  • [21] Sumana IJ, Islam MM, Zhang D, Lu G. Content based image retrieval using curvelet transform. In: 10th IEEE Workshop on Multimedia Signal Processing; 2008. pp. 11-16.