Muneesawang P, Guan L. An interactive approach for CBIR using a network of radial basis functions[J]. IEEE Trans on Multimedia, 2004, 6(5): 703-716.
[2]
Guo Z H, Zhang L, Zhang D. A completed modeling of local binary pattern operator for texture classification[J]. IEEE T Image Process, 2010, 19(6): 1657-1663.
[3]
Shrivastava N, Tyagi V. Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching[J]. Information Sciences, 2014, 25: 212-224.
[4]
Liu Y, Zhang D, Lu G, et al. A survey of content-based image retrieval with high-level semantics[J]. Pattern Recognition, 2007, 40(1): 262-282.
[5]
Elalami M. A new matching strategy for content based image retrieval system[J]. Applied Soft Computing, 2014, 14: 407-418.
[6]
Liu L, Fieguth P W. Texture classification from random features[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2012, 34(3): 574-586.
(Liu J P, Gui W H,Tang Z H. Analysis of the best production condition of cleaner froth in bauxite flotation process based on froth texture coarseness measurement[J]. Control and Decision, 2013, 28(7): 1013-1017.)
(Liu J P, Gui W H, Mu X M, et al. Flotation froth image texture feature extraction based on Gabor wavelets[J]. Chinese J of Scientific Instrument, 2010, 31(8): 1769-1775.)
[16]
Aujol J-F, Gilboa G, Chan T, et al. Structure-texture image decomposition-modeling, algorithms, and parameter selection[J]. Int J of Computer Vision, 2006, 67(1): 111-136.
[17]
Povlow B R, Dunn S M. Texture classification using noncasual hidden Markov models[C]. Proc of the Computer Vision And Pattern Recognition. New York, 1993: 642-643.
[18]
Li L, Yunli L, Fieguth P W, et al. BRINT: Binary rotation invariant and noise tolerant texture classification[J]. IEEE Trans on Image Processing, 2014, 23(7): 3071-3084.
[19]
Geusebroek J-M, Smeulders A W. A six-stimulus theory for stochastic texture[J]. Int J of Computer Vision, 2005, 62(1/2): 7-16.
[20]
Brown W K. A theory of sequential fragmentation and its astronomical applications[J]. J of Astrophysics and Astronomy, 1989, 10(1): 89-112.
[21]
Freeman W T, Adelson E H. The design and use of steerable filters[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1991, 13(9): 891-906.
(Zhang T, HongWX.Weight calculation for computational geometry combining classifier using fuzzy of class space[J]. Control and Decision, 2013, 28(4): 569-573.)
[24]
Chevallier S, Bertrand D, Kohler A, et al. Application of PLS-DA in multivariate image analysis[J]. J of Chemometrics, 2006, 20(5): 221-229.
(Dong X F, Dai L K, Huang C W. Near-infrared spectroscopy soft-sensing method by combining partial least squares discriminant analysis and support vector machine[J]. J of Zhejiang University: Engineering Science, 2012, 46(5): 824-829.)
[27]
Haralick R M, Shanmugam K, Dinstein I H. Textural features for image classification[J]. IEEE Trans on Systems, Man, and Cybernetics, 1973, 3(6): 610-621.
[28]
Manjunath B S, MaW-Y. Texture features for browsing and retrieval of image data[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1996, 18(8): 837-842.
[29]
Mao J, Jain A K. Texture classification and segmentation using multiresolution simultaneous autoregressive models[J]. Pattern Recognition, 1992, 25(2): 173-188.
[30]
Wang L, Liu J. Texture classification using multiresolution Markov random field models[J]. Pattern Recognition Letters, 1999, 20(2): 171-182.
[31]
Suykens J, Van Gestel T, De Brabanter J, et al. Least squares support vector machines[M]. Singapore: World Scientific, 2002: 71-116.