International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 8- Feb 2014 Image Classification Methods Nishana Rasheed#1, Shreeja.R*2 # * Department of Computer Science and Engineering, MES College of Engineering, Kuttippuram, Calicut University, India Department of Computer Science and Engineering, Assistant Professor, MES College of Engineering, Kuttippuram, Calicut University, India Abstract— The problem of image classification has aroused considerable research interest in the field of image processing. Traditional methods often convert an image to a vector and then use a vector-based classifier. A novel multiple rank regression model (MRR) for matrix data classification is used. Unlike traditional vector-based methods, multiple left projecting vectors and right projecting vectors are employed to regress each matrix data set to its label for each category. The convergence behavior, initialization, computational complexity of different classification methods are analysed.Compared with vector-based regression methods, MRR achieves higher accuracy and has lower computational complexity. Compared with traditional supervised tensor-based methods, MRR performs better for matrix data classification. is only approximated locally and all computation is deferred until classification. The k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). Keywords— pattern recognition, computer vision, classification, rank of regression, fitting error, learning capacity, singularity problem. INTRODUCTION Classification algorithms are based on the assumption that image depicts one or more features and each of these features belong to one of the several distinct and exclusive classes. Image such as face images, palm images, or MRI[8] data are usually represented in the form of data matrices. Additionally, in video data mining, the data in each time frame is also a matrix. How to classify this kind of data is one of the most important topics for both image processing and machine learning. In literature survey several classification approaches have been proposed such as KNN,SVM,1DREG,LDA,2DLDA,GBRand MRR . If k = 1, then the object is simply assigned to the class of that single nearest neighbor. Fig 1 K-Nearest Neighbor Classifier B. Support Vector Machine LITERATURE SURVEY A. K- Nearest Neighbour Classifier (KNN) K-NearestNeighbourhood classifier (KNN)[1] by G. Shakhnarovich which is similarity based. In pattern recognition, the k-nearest neighbors algorithm (KNN) is a non-parametric method for classification and regression, that predicts objects' "values" or class memberships based on the k closest training examples in the feature space. KNN is a type of instance-based learning, or lazy learning where the function ISSN: 2231-5381 Support Vector Machine (SVM) [2] by V. N. Vapnik which is margin based. In SVM a data point is viewed as a p-dimensional vector and separate such points with a (p 1)-dimensional hyperplane. This is called a linear classifier. There are many hyperplanes that might classify the data reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes . http://www.ijettjournal.org Page 461 International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 8- Feb 2014 In fig(3) Direction W is taken such that both differences between the class means projected on to these directions µ1 and µ2 is large and variance(σ1 and σ1 ) around these mean is small . Fig 2 Support Vector Machine C. One Dimensional Regression(1DREG) Among these approaches, due to their simplicity, effectiveness and inductive nature one dimensional Regression methods ( denoted as 1DREG ) [3] by C. M. Bishop regression methods have been widely used in many real applications. 1DREG is a representative method in vector-based regression works. It is also a famous model for classification. Denote the matrix data Xi(ith training matrix data) as an mn- dimensional vector data xi by connecting each row (or column). 1DREG aims to regress each data to its label vector by computing c transformation vectors and constants.1DREG converts the matrix data into a vector. Thus, it will lose the correlation of matrix data and its computational time consuming is unacceptable if the matrix scale is large. . D .General Bilinear Regression(GBR) As mentioned in [6], GBR is the two-dimensional counterpart of 1DREG. It replaces the regression function of 1DREG by a bilinear regression function Besides, it only uses one left projecting vector together with one right projecting vector. Its fitting error is too large for some real regression problems. E .Linear Discriminant Analysis(LDA) Linear Discriminant Analysis projects data on to a lower dimensional vector space such that the ratio of between-class distance to within class distance is maximized thus achieving maximum discrimination between classes. It suffers from singularity problem. It is based on maximizing the distance means of the classes Fig3 Linear Discriminant Analysis.. F. Two Dimensional Linear Discriminant Analysis (2DLDA) Two-dimensional Linear Discriminant Analysis (2DLDA) [4] by J. Ye, R. Janardan is a popular supervised tensor based approach. 2DLDA aims to find two transformation matrices L and R,which map Xi to its low dimensional embedding, i.e., Zi, by Zi = LTXiR. It tries to minimize the within-class distance Dw and maximize the between-class distances Db. G. Multiple Rank Regressions (MRR) Multiple rank regression model (MRR)[5] is meant for matrix data classification . Unlike traditional vector-based methods, multiple-rank left projecting vectors and right projecting vectors to regress each matrix data set to its label for each category is employed. MRR achieves higher accuracy and has lower computational complexity. Compared with traditional supervised tensor-based methods, MRR performs better for matrix data classification. J(W ) = argmaxW (m1 − m2 )2 /(σ1 + σ1 ). ISSN: 2231-5381 http://www.ijettjournal.org Page 462 International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 8- Feb 2014 TABLE 2 ACCURACY OF DIFFERENT CLASSIFICATION METHODS Fig 4 Multiple Rank Regression MRR is a combination of multiple two category classifiers via one versus rest strategy. More concretely, in training the classifier for the r-th category, the labels for the points who belong to the r-th category are one. If a point does not belong to this class, its label is zero. In the proposed system multiplerank left projecting vectors and right projecting vectors are employed to regress each matrix dataset to its label for each category .Compared with traditional supervised tensor-based methods, MRR can performs better for matrix data classification. Computational complexity is more for uncorrelated data in this method.MRR can be extended for unsupervised and semi supervised cases. PERFORMANCE ANALYSIS TABLE 1 PERFORMANCE ANALYSIS BASED ON DIFFERENT PARAMETERS Parameter 1DREG GBR MRR Rank of Regression Fitting Error Largest Weakest Moderate Smallest Too large Trade off Learning Capacity Strongest Weakest Trade off Speed Slowest Fastest Moderate Accuracy Moderate Moderate Highest ISSN: 2231-5381 TABLE 3 COMPUTATIONAL TIME OF DIFFERENT CLASSIFICATION METHODS CONCLUSIONS It was found that LDA suffered from singularity problem .By applying intermediate dimension reduction singularity problem was reduced. No singularity problem was seen in 2DLDA.1DREG suffered from over fitting problem. 1DREG has smallest fitting error hence strongest learning capacity.GBR has weakest capacity for learning but strongest capacity for generalization. MRR for data matrix classification is different from traditional regression approaches which reformulate the matrix data into a vector and use only one projecting vector, we have used several left projecting vectors and the same number of right projecting vectors to regress each matrix data to its label for each category. Essentially, MRR can be regarded as a trade-off between the capacity of learning and generalization for regression. MRR not only achieve satisfied accuracy, but also has low computational complexity. http://www.ijettjournal.org Page 463 International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 8- Feb 2014 REFERENCES [1] ]Salim Bettahar, Amine Boudghene Stambouli, Patrick Lambert, “Nearest Neighbor methods in Learning and vision: Theory and practice MIT Press-2006. [2] V. N. Vapnik, "The Nature of Statistical Learning Theory. ", New York: Springer-Verlag, 1995. [3] C. M. Bishop, "Pattern Recognition and Machine Learning ", Secaucus, NJ: Springer Verlag, 2006. [4] J. Ye, R. Janardan, and Q. Li, "Two dimensional linear discriminant analysis ", in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2004. [5] Chenping Hou, Member, IEEE, Feiping Nie, Dongyun Yi, and Yi Wu , "Efficient Image Classification via Multiple Rank Regression ", IEEE Transaction On Image Processing, January 2013. [6] K. R. Gabriel, Generalised bilinear regression ", Biometrika, vol. 85,no. 3, pp. 689700, 1998. [7] H. GOVIL , M. KUMAR and S. FAROOQ , “Comparative evaluation of fuzzy based object oriented image classification method with parametric and non-parametric classifier", 14th annual conference on geospatial information technology and application,2012. [8] A. W.-K. Kong, D. D. Zhang, and M. S. Kamel, ”A survey of palmprint recognition ", Pattern Recognition” , vol. 42, no. 7, pp. 14081418, 2009 . ISSN: 2231-5381 http://www.ijettjournal.org Page 464