Raisin Quality Classification Using Least Squares Support Vector Machine (LSSVM) Based on Combined Color and Texture Features

In this paper, an approach based on combined color and texture features to classify raisins is presented. Least squares support vector machine (LSSVM), linear discriminant analysis, and soft independent modeling of class analogy were used to construct classification models. A total of 480 images were captured from four grades of raisin samples by a Basler 601 fc IEEE1394 digital camera, 200 images were randomly selected to create calibration model (training set), and remaining images were used to verify the model (prediction set). Color features and texture features were obtained from two color spaces: red–green–blue and hue–saturation–intensity using histogram method and gray level co-occurrence matrix method, respectively. Our results indicate that the best performance with about 95% of average correct answer rate is achieved by LSSVM using combined color and texture features from HSI color space. This result is significantly higher than the performance of solely used color or texture features. The combined color and texture features coupled with a LSSVM classifier are a highly accurate way for raisin quality classification.

[1]  Vapnik,et al.  SVMs for Histogram Based Image Classification , 1999 .

[2]  Yiyu Cheng,et al.  Discriminating the Genuineness of Chinese Medicines Using Least Squares Support Vector Machines , 2006 .

[3]  M. J. Delwiche,et al.  Raisin Grading by Machine Vision , 1993 .

[4]  Zhu Shan-an.  Face recognition based on two-dimensional image principal component analysis , 2007 .

[5]  W. R. Buckland,et al.  Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability. , 1952 .

[6]  Huan Liu,et al.  Book review: Machine Learning, Neural and Statistical Classification Edited by D. Michie, D.J. Spiegelhalter and C.C. Taylor (Ellis Horwood Limited, 1994) , 1996, SGAR.

[7]  A. Belousov,et al.  A flexible classification approach with optimal generalisation performance: support vector machines , 2002 .

[8]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[9]  Mahmoud Omid,et al.  Sorting Raisins by Machine Vision System , 2010 .

[10]  Calyampudi R. Rao.  The use and interpretation of principal component analysis in applied research , 1964 .

[11]  Sanghoon Kim,et al.  Computational Analysis of PCA-based Face Recognition Algorithms , 2003 .

[12]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[13]  He Dong-jian.  Research of Classification of Raisin Based on BP Networks , 2007 .

[14]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[15]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[16]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[17]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[18]  Chu Zhang,et al.  Early Detection of Botrytis cinerea on Eggplant Leaves Based on Visible and Near-Infrared Spectroscopy , 2008 .

[19]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[20]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[21]  Osamu Sakata,et al.  Basic Study on Grading of Chinese Dried Green Raisin Using Image Information (Part 2) , 2003 .

[22]  Sabine Van Huffel,et al.  Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines , 2003, Artif. Intell. Medicine.

[23]  Johan A. K. Suykens,et al.  Multiclass LS-SVMs: Moderated Outputs and Coding-Decoding Schemes , 2002, Neural Processing Letters.

[24]  S. Wold,et al.  SIMCA: A Method for Analyzing Chemical Data in Terms of Similarity and Analogy , 1977 .

[25]  Yong He,et al.  Variety Identification of Chinese Cabbage Seeds Using Visible and Near-Infrared Spectroscopy , 2008 .

[26]  Xiaoling Li,et al.  Level Detection of Raisins Based on Image Analysis and Neural Network , 2009, ISNN.

[27]  Kevin W. Bowyer,et al.  Empirical evaluation techniques in computer vision , 1998 .

[28]  M. G. O'shea,et al.  Role of chemometrics for at-field application of NIR spectroscopy to predict sugarcane clonal performance , 2007 .

[29]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[30]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[31]  Da-Wen Sun,et al.  Recent applications of image texture for evaluation of food qualities—a review , 2006 .

[32]  Quansheng Chen,et al.  Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM). , 2007, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[33]  Wu Di,et al.  [Application of multispectral image texture to discriminating tea categories based on DCT and LS-SVM]. , 2009, Guang pu xue yu guang pu fen xi = Guang pu.

[34]  Zhiyuan Zeng,et al.  Remote Sensing Image Classification Based on the HSI Transformation and Fuzzy Support Vector Machine , 2009, 2009 International Conference on Future Computer and Communication.

[35]  M. Omid,et al.  Implementation of an Efficient Image Processing Algorithm for Grading Raisins , 2010 .

[36]  Lanwei Zhang,et al.  Extraction and Enzymatic Hydrolysis of Inulin from Jerusalem artichoke and their Effects on Textural and Sensorial Characteristics of Yogurt , 2010 .

[37]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[38]  Gauri S. Mittal,et al.  Rapid Detection of Microorganisms Using Image Processing Parameters and Neural Network , 2010 .

[39]  Bruce E. Mackey,et al.  Near Infrared Analysis Potential for Grading Raisin Quality and Moisture , 1995 .

[40]  Da-Wen Sun,et al.  CORRELATING IMAGE TEXTURE FEATURES EXTRACTED BY FIVE DIFFERENT METHODS WITH THE TENDERNESS OF COOKED PORK HAM: A FEASIBILITY STUDY , 2006 .

[41]  Laura A. Ramallo,et al.  Gluten-free Bread Based on Tapioca Starch: Texture and Sensory Studies , 2012, Food and Bioprocess Technology.

[42]  Gary Williamson,et al.  Polyphenol content and health benefits of raisins. , 2010, Nutrition research.

[43]  T. F. Burks,et al.  CLASSIFICATION OF WEED SPECIES USING COLOR TEXTURE FEATURES AND DISCRIMINANT ANALYSIS , 2000 .