Feature extraction method for digital images based on intuitionistic fuzzy local binary pattern

Feature extraction is an important step in the field of digital image processing, which also helps in reducing the dimensions from large data. Researchers are investigating an efficient methods as there are lot of challenges in extracting significant features from an image that can reveal essential information. However, a very small work has been reported to this research domain in the last decades. In this paper, we propose intuitionistic fuzzy local binary (IFLBP) for extracting texture feature from the input image. The proposed technique extends fuzzy local binary pattern method by including intuitionistic fuzzy set theory in the demonstration of local patterns of texture in images. The proposed algorithm has been applied on various images and obtained result shows the effectiveness of our proposed technique.

[1]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[2]  Eduard Montseny,et al.  Fuzzy Texture Unit and Fuzzy Texture Spectrum for texture characterization , 2007, Fuzzy Sets Syst..

[3]  Chi-Ho Chan.  Multi-scale local Binary Pattern Histogram for Face Recognition , 2007, ICB.

[4]  Nikos Dimitropoulos,et al.  Thyroid Texture Representation via Noise Resistant Image Features , 2008, 2008 21st IEEE International Symposium on Computer-Based Medical Systems.

[5]  Li Ma,et al.  Optimum Gabor filter design and local binary patterns for texture segmentation , 2008, Pattern Recognit. Lett..

[6]  Nikolay N. Ponomarenko,et al.  Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing , 2005, EURASIP J. Adv. Signal Process..

[7]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Jukka Iivarinen,et al.  Surface defect detection with histogram-based texture features , 2000, SPIE Optics East.

[9]  Dimitrios K. Iakovidis,et al.  Noise-robust statistical feature distributions for texture analysis , 2008, 2008 16th European Signal Processing Conference.

[10]  Dimitrios K. Iakovidis,et al.  Fuzzy Local Binary Patterns for Ultrasound Texture Characterization , 2008, ICIAR.

[11]  Jun Kong,et al.  Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development , 2009, Pattern Recognit..

[12]  Yan Li,et al.  Remote Sensing Texture Analysis Using Multi-Parameter and Multi-Scale Features , 2003 .

[13]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[14]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[15]  Tao Wang,et al.  Markov chain local binary pattern and its application to video concept detection , 2008, 2008 15th IEEE International Conference on Image Processing.

[16]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[17]  Shengcai Liao,et al.  Illumination Invariant Face Recognition Using Near-Infrared Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Mohd Dilshad Ansari,et al.  An Efficient Salt and Pepper noise Removal and Edge preserving Scheme for Image Restoration , 2012 .

[19]  Dimitris K. Iakovidis,et al.  Fuzzy binary patterns for uncertainty-aware texture representation , 2012 .

[20]  Keun Ho Ryu,et al.  Rotation and Gray-Scale Invariant Classification of Textures Improved by Spatial Distribution of Features , 2005, DEXA.

[21]  Shu-Yuan Chen,et al.  Retrieval of translated, rotated and scaled color textures , 2003, Pattern Recognit..

[22]  Joan Batlle,et al.  Detection of matchings in a sequence of underwater images through texture analysis , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[23]  Bertrand Zavidovique,et al.  Median Binary Pattern for Textures Classification , 2007, ICIAR.

[24]  S. P. Ghrera,et al.  Pixel-Based Image Forgery Detection: A Review , 2014 .

[25]  Olli Silvén,et al.  Wood Inspection With Non-Supervised Clustering , 2000 .

[26]  Hadi Hadizadeh,et al.  Random Texture Defect Detection Using 1-D Hidden Markov Models Based on Local Binary Patterns , 2008, IEICE Trans. Inf. Syst..

[27]  Samy Bengio,et al.  A Discriminative Kernel-Based Approach to Rank Images from Text Queries , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Ehsanollah Kabir,et al.  Fabric Defect Detection Using Modified Local Binary Patterns , 2008, EURASIP J. Adv. Signal Process..

[29]  Jun-Hai Yong,et al.  Texture Analysis and Classification With Linear Regression Model Based on Wavelet Transform , 2008, IEEE Transactions on Image Processing.

[30]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[31]  Bertrand Zavidovique,et al.  Rotationally Invariant Hashing of Median Binary Patterns for Texture Classification , 2008, ICIAR.