Abstract:Because the performance of the image retrieval system could be effectively improved by using the complementary features, a retrieval method of the texture image using L1 energy and generalized Gaussian distribution parameter features was proposed in the improved Contourlet transform domain. Firstly, the directional subband coefficients went through generalized Gaussian modeling with an improved approach. Then, the texture images were respectively retrieved based on the single feature and the corresponding similarity measurement. Lastly, using the complementary features and the direct summation of their similarity measurements, the texture images were retrieved. The experimental results show that, compared with single feature, the average retrieval rates of the texture image database are effectively improved by the complementary features that fully represent the structural information and the random distribution information.
DATTA R, JOSHI D, LI JIA, et al. Image retrieval: Ideas, influences, and trends of the new age [J]. ACM Computing Surveys, 2008, 40(2): 1-60.
[2]
RAO A R, LOHSE G L. Towards a texture naming system: identifying relevant dimensions of texture [J]. Vision Research, 1996, 36 (11): 1649-1669.
[3]
MANJUNATH B S, MA W Y. Texture features for browsing and retrieval of image data [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(8):837-842.
[4]
RANDEN T, HUSOY J H. Filtering for texture classification: A comparative study [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(4): 291-310.
[5]
DO M N, VETTERLI M. Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance [J]. IEEE Transactions on Image processing, 2002, 11(2): 146-158.
[6]
KOKARE M, BISWAS P K, CHATTERJI B N. Texture image retrieval using new rotated complex wavelet filters [J]. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 2005, 35(6): 1168-1178.
[7]
DO M N, VETTERLI M. The contourlet transform: an efficient directional multiresolution image representation [J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.
[8]
LU YUE, DO M N. A new contourlet transform with sharp frequency localization [C]// Proceedings of IEEE International Conference on Image Processing. Atlanta: IEEE, 2006: 1629-1632.
[9]
HEEGER D, BERGER J R. Pyramid-based texture analysis/synthesis [C]//Proceedings of International Conference on Image Processing. New York: ACM Press, 1995: 648-651.
[10]
OJALA T, PIETIKAINEN M, HARWOOD D. A comparative study of texture measures with classification based on feature distributions [J]. Pattern Recognition, 1996, 29 (1): 51-59.
[11]
VETTERLI M, KOVACEVIC J. Wavelets and subband coding [M]. Englewood Cliffs: Prentice Hall PTR, 1995.
[12]
QU HUAI-JING, PENG YU-HUA, SUN WEI-FENG. Texture image retrieval based on contourlet coefficient modeling with generalized Gaussian distribution [C]// Proceedings of Second International Symposium on Advances in Computation and Intelligence. Berlin: Springer, 2007: 493-502.
[13]
LIU XIU-WEN, WANG DE-LIANG. Texture classification using spectral histograms [J]. IEEE Transactions on Image Processing, 2003, 12(6): 661-670.
[14]
MOULIN P, LIU JUAN. Analysis of multiresolution image denoising scheme using generalized Gaussian and complexity priors [J]. IEEE Transactions on Information Theory, 1999, 45(3): 909-919.
[15]
OLSHAUSEN B A, FIELD D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images [J]. Nature, 1996, 381: 607-609.
[16]
VO AN P N, ORAINTARA S, NGUYEN T T. Using phase and magnitude information of the complex directional filter bank for texture image retrieval [C]// Proceedings of IEEE International Conference on Image Processing. San Antonio: IEEE, 2007: 61-64.