This paper proposed an efficient texture retrieval method for indexing images with heavy-tailed distribution, such as biomedical images, sonar images and natural texture images. In the proposed scheme, a multivariate Log-Gaussian mixture model (MLGMM) was used to model the sharp peaks, heavy tails, and even the multimodal statistical properties of two-dimension Gabor coefficients of texture under different scales and orientations. The parameters of MLGMM are estimated by expectation maximum (EM). In our scheme, each class of texture is modeled by one MLGMM and Bayesian classification is implemented by feeding the output of MMLGM into the Bayesian classifier. Experiments on feature extraction and similarity measurement have been done to demonstrate the effectiveness of our proposed algorithm. Extensive experiments have validated that our retrieval scheme has an average retrieval rate of 2% higher than other related texture statistical techniques.
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We used public database for experiments, all data can be downloaded from the web.
We reconstruct the code downloaded from the web (http://www.pudn.com/Download/item/id/1274555.html) for estimating the parameters of MLGMM. Our code will be opened after our several immediate articles are completed.
Aitchison J, Ho CH (1989) The multivariate poisson-Log normal distribution. Biometrika 76(4):643–653
Backes AR, Casanova D, Bruno OM (2012) Color texture analysis based on fractal descriptors. Pattern Recogn 45(5):1984–1992
Basso RM, Lachos VH, Cabral CRB, Ghosh P (2011) Robust mixture modeling based on scale mixtures of skew-normal distributions. Comput Stat Data Anal 54(12):2926–2941
Bingqian HE, Wei W, Yanbei S, Lianxin G, Bin Z (2019) Human motion recognition based on spatiotemporal interest points and multivariate generalized gaussian mixture models. J Chengdu Univ Inf Technol 10:358–364
Chen X, Zhang G (2017) Non-gaussian gabor filters for biometric feature extraction. Comput Eng Appl 8:170–175
Chen Xi, Zhou Z, Zhang J, Liu Z (2016) Qingsong Huang. Local convex-and-concave pattern: an effective texture descriptor. Inf Sci 363:120–139
Chen Y, Wang J, Liu S, Chen X, Yang K (2019) Multiscale fast correlation filtering tracking algorithm based on a feature fusion model. Concurrency and Computation Practice and Experience (5)
Chen Z et al (2020) Construction of a hierarchical feature enhancement network and its application in fault recognition. IEEE Trans Industr Inf. https://doi.org/10.1109/TII.2020.3021688
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Royal Stat Soc (Series B) 39(1):1–38
Evangelidis DG, Horaud R (2017) Joint alignment of multiple point sets with batch and incremental expectation-maximization. IEEE Trans Pattern Anal Mach Intell 99:1–14
Fang KT, Fang KT, Kotz S (2007) On the Student’s t-distribution and the t-statistic. J Multivar Anal 98(6):1293–1304
Field DJ (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am 4(12):2379–2394
Haritha D, Rao CSKS (2012) Face recognition algorithm based on doubly truncated Gaussian mixture model using DCT coefficients. Int J Comput Appl 39(9):23–28
Juang BH, Levinson SE, Sondhi MM (2016) Maximum likelihood estimation for multivariate mixture observations of markov chains. IEEE Trans Inf Theory 32(2):307–309
Kim SC, Kang TJ (2007) Texture classification and segmentation using wavelet packet frame and Gaussian mixture model. Pattern Recogn 40(4):1207–1221
Kuruoglu EE, Zerubia J (2003) Skewed α-stable distributions for modelling textures. Pattern Recogn Lett 24(1–3):339–348
Lamba S, Nain N (2018) A texture based manifold approach for crowd density estimation using Gaussian Markov Random Field. Multimed Tools Appl 78:1–20
Lange KL, Little RJA, Taylor JMG (1989) Robust statistical modeling using the t- distribution. Publ Am Stat Assoc 84(408):881–896
Lee SX, Mclachlan GJ (2012) On the fitting of mixtures of multivariate skew t-distributions via the EM algorithm. J Cell Sci 114(10):1893–1900
Li SZ, Jain AK (2004) Handbook of face recognition. Springer, Verlag
Li C, Huang Y, Zhu L (2017) Color texture image retrieval based on Gaussian copula models of Gabor wavelets. Pattern Recogn 64:118–129
Lin TI (2009) Maximum likelihood estimation for multivariate skew normal mixture models. J Multivar Anal 100(2):257–265
Lin TI (2010) Robust mixture modeling using multivariate skew t distributions. Stat Comput 20(3):343–356
Lingyun X, Guohan Z, Qian L, Wei H, Feng L (2018) Tumk-elm: a fast unsupervised heterogeneous data learning approach. IEEE Access, pp 1–1
Lu H, Li Y, Mu S et al (2017a) Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J 5(4):2315–2322
Lu H, Li Y, Chen M et al (2017b) Brain intelligence: go beyond artificial intelligence. Mobile Netw Appl 23(2):368–375
Lu H, Yu T, Sun Y (2020a) DRRS-BC: Decentralized Routing Registration System Based on Blockchain. IEEE/CAA Journal of Automatica Sinica
Lu H, Zhang M, Xu X, Li Y, Shen HT (2020b) Deep fuzzy hashing network for efficient image retrieval. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.2984991
Lu H, Yang R, Deng Z, Zhang Y, Gao G, Lan R (2020c) Chinese image captioning via fuzzy attention-based DenseNet-BiLSTM. ACM Transactions on Multimedia Computing Communications and Applications
Lu H, Zhang Y, Li Y et al (2020d) User-oriented virtual mobile network resource management for vehicle communications. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.2991766
Lu W, Zhang Y, Wang S, Huang H, Liu Q, Luo S (2020e) Concept representation by learning explicit and implicit concept couplings. IEEE Intell Syst. https://doi.org/10.1109/MIS.2020.3021188
Lu W, Zhang X, Lu H, Li F (2020f) Deep hierarchical encoding model for sentence semantic matching. J Vis Commun Image Represent 71:102794
Lynch AG (2008) Log normal distribution. Springer, New York
Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–692
Mallat SG (1998) A wavelet tour of signal processing. Academic, New York
Matza A, Bistritz Y (2014) Skew Gaussian mixture models for speaker recognition. Signal Process Iet 8(8):860–867
Mihcak MK, Kozintsev I, Ramchandran K, Moulin P (1999) Low complexity image denoising based on statistical modeling of wavelet coefficients. IEEE Signal Process Lett 6(12):300–303
Nakajima J, Omori Y (2012) Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student’s t-distribution. Comput Stat Data Anal 56(11):3690–3704
Picard RW, Kabir T, Liu F (1993) Real-time recognition with the entire Brodatz texture database. Computer Vision and Pattern Recognition. Proceeding CVPR '93. 1993 IEEE Computer Society Conference on. IEEE.
Picard R, Graczyk C, Mann S et al (2010) VisTex vision texture database. https://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html
Savitsky TD, Toth D (2016) Bayesian estimation under informative sampling. J Am Stat Assoc 10:1–35
Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40(1):41–50
Shao M, Nikias CL (1993) signal processing with fractional lower order moments stable process and its application. Proc IEEE 81(7):986–1010
Shojaei SR, Hosseini Nassiri V et al (2011) Mixture of skewed alpha-stable distributions. Aip Conf Proc 1305(1):130–137
Snijders TAB (2002) Markov chain monte carlo estimation of exponential random graph models. J Soc Struct 2:2–40
Tzagkarakis G, Beferull-Lozano B, Tsakalides P (2008) Rotation invariant texture retrieval via signature alignment based on steerable sub-gaussian modeling. IEEE Trans Image Process 17(7):1212–1225
Unser M (1995) Texture classification and segmentation using wavelet frames. IEEE Trans Image Process 4(11):1549–1560
Wang P, Wang D, Zhang X, Li X, Tian X (2020) Numerical and experimental study on the maneuverability of an active propeller control based wave glider. Appl Ocean Res 104:102369
Wu P, Manjunath BS, Newsam S, Shin HD (2000) A texture descriptor for browsing and similarity retrieval. Signal Process Image Commun 16(1):33–43
Xu X, Lu H, Song J et al (2020) Ternary Adversarial networks with self-supervision for zero-shot cross-modal retrieval. IEEE Trans Cybern 50(6):2400–2413
Yin ZY, Peng SL, Ren H, Guo Q, Chen ZH (2005) Log Cauchy, log-sech and lognormal distributions of species abundances in forest communities. Ecol Model 184(2):329–340
This work was supported in part by the National Natural Science Foundation of China under Grant (61762022), the doctoral startup fund of Guizhou Normal University 2017, under Grant 0517075 and the special project of academic new seedling cultivation and innovation exploration in 2017 under Grant 5726.
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Chen, X., Li, Y. & Zhou, Z. Texture retrieval based on multivariate Log-Gaussian mixture model. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-02895-6
- Multivariate Log-Gaussian mixture model
- Parameters estimation
- Bayesian classification
- Gabor filters
- Texture retrieval