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Research Article
 

Improvement of Outliers Detection in Image Classification



Asma Lak, Naser Parhizgar and Mahbobeh Lak
 
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ABSTRACT

In this study a method has been proposed by using of the local information of image pixels in outlier identification to reduce the time of image classification. The applied algorithm is Expectation Maximization (EM algorithm) which is an iterative algorithm. In this algorithm, in each step, outliers are detected then removed in order to prevent error propagation in next steps. By decreasing the errors in each step the validation and accuracy of the classification is increased. Thus we proposed a method which use from the mean entropy of pixels which are in neighborhood of first, second and third edge pixels of mixture to the image. By using of this method the time of classification of a typical image (AVIRIS hyperspectral image) has been improved.

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  How to cite this article:

Asma Lak, Naser Parhizgar and Mahbobeh Lak, 2015. Improvement of Outliers Detection in Image Classification. Journal of Applied Sciences, 15: 1133-1137.

DOI: 10.3923/jas.2015.1133.1137

URL: https://scialert.net/abstract/?doi=jas.2015.1133.1137
 
Received: June 12, 2015; Accepted: August 29, 2015; Published: September 14, 2015



INTRODUCTION

Labels are difficult to obtain and unlabeled data is abundant for image pixels, therefore semi-supervised learning is a good idea to reduce human labor and improve classification accuracy (Rosset et al., 2008). Von Luxburg et al. (2005) has been presented a method for hyperspectral images classification. Two data mining techniques to detect outliers are: The Bay’s algorithm for distance-based outliers (Bay and Schwabacher, 2003) and a density-based local outlier algorithm (Breunig et al., 2000). There are some pixels in semi-supervised classification which are labeled wrongly. This problem affects on estimation, covariance matrix and means value and finally leads to decreasing the accuracy and validation of classification. These samples are called outlier. By using of outlier detection in multivariate analysis these pixels can be identified and before implementing of multivariate methods they can be used as a preprocessing. At the end of this prepossessing these pixels are removed and the destructive effects of them on classification are eliminated (Lak et al., 2013).

In this study edge pixels of mixtures (boundary samples) of any class of image are determined and mean entropy value of these boundary samples are used. This trend is done in any stage of EM algorithm and outliers in each stage are identified then removed and don’t use in the next stages of the algorithm (Lak et al., 2013).

The aim of this study is to propose a method for more accuracy of outlier detection which reduces the repetition rate of the EM algorithm and reduces the time of classification finally.

MATERIALS AND METHODS

The image which is used in this study related to agriculture-forest area which is taken by AVIRIS in June 1992 in Indiana. This image has 145×145 pixels and radiometric accuracy is 8 bits. The main problem was that most plants of this area are corn and soya. They are at the beginning of the period of growth. In other hand, that area was an experimental site and some of the cultivated products of previous years remained on it. Based on cultivated plants and conditions of ground each area has 16 various classes (Rosset et al., 2008; Von Luxburg et al., 2005).

Outlier identification: When the image by using of number of classes is classified (these classes are determined formerly) the pixels are placed in the classes and any image is labeled. May be some of these estimation labels are wrong for some pixels. These samples which are labeled wrongly called "outlier". The EM algorithm has been used to classification (Acuna and Rodriguez, 2004). This algorithm is iterative and each stage uses the results of previous stage. Therefore, if an error is occurred, it will enter in next stage and repeat. As a result the errors propagate in next stages. Proposed method is comparison of value of "Entropy×weight" for any pixels with "Mean entropy×weight" for any sample of each class. If the image has i class and any class has m mixture, by estimation of mean vector μi and covariance matrix Σi for each class, i, outlier are detected as: one stage of EM is run and p(x|i) is calculated that is probability of belonging of x to i class.

Value of entropy for all pixels of image is:

By taking in account of training samples the mixture of each class is determined and by using an edge detector the boundary samples are identified. The boundary samples of mixture of each class means that, pixels which are placed at the boundary of a mixture in the training labeled samples. For boundary samples of each mixture, the neighbor pixels are determined by:

where, Fi, Se and Th are first, second and third neighborhood, respectively. Since, some of samples of neighbor of pixel "x" that are not labeled may be belonging to another class, the entropy of each neighbor is multiplied in pixels weight. The mean is calculated for these three obtained values. The final mean is calculated for mixtures of each class. To outlier identification in next step, the entropy of each x pixel is compared with final mean of each i class. If the entropy value is smaller than mean value, x pixel is identified as an outlier i for class.

The detected outliers of each class are removed (Becker and Gather, 1999, 2001) in the next stage are classified again. The identification of outliers in each stage of EM algorithm is done. The number of repetition is equal to the number of EM repetition and continues to stop condition of algorithm. In this study 1346 training sample and 2105 experimental samples has been used.

EM algorithm: Mixture model has long been used for semi-supervised learning, e.g. Gaussian Mixture Model (GMM). More information can be found in Ratsaby and Venkatesh (1995), Nigam and Ghani (2000) and Castelli and Cover (1996). In this model training is typically done with the EM algorithm. It has several advantages: The model is inductive and handles unseen points naturally and it is a parametric model with a small number of parameters.

In typical mixture model for classification, the generative process is the following 6: one first select a class y, then chooses a mixture component m ∈ {1,..... M} with p(m|y) and finally a point x according to p(m|y) is generated. Thus:

RESULTS

Figure 1a-b shown the classified image in the first and final stages of the algorithm. Actual classification of image is according to Fig. 2. By the comparison between these three images it can be seen the effect of the improved algorithm on classification improvement. X and Y axis show the coordinates of each pixel of the hyperspectral image.

Figure 3 shows the variations of classification validity for some class and total accuracy for classification.

Figure 4 shows the comparison between proposed method and previous ones in related to spent time for each repetition of algorithm. As it can be seen by improving algorithm, the time of each repetition and total time has been reduced by reduction of number of repetition.

Fig. 1(a-b): (a) First step and (b) Final step of the proposed algorithm

Fig. 2: Thematic image

Fig. 3: Accuracy for some classes and total accuracy

DISCUSSION

Previous study in this area includes modifying the clustering objective function so that it includes a term for satisfying specified constraints (Demiriz et al., 1999) and enforcing constraints to be satisfied during the cluster assignment in the clustering process (Wagstaff et al., 2001).

Fig. 4:Comparison of the time of each step of EM algorithm with previous method

Zhu et al. (2003) has been introduced an approach to semi-supervised learning based on a Gaussian random field model defined with respect to a weighted graph representing labeled and unlabeled data. Promising experimental results have been presented for text and digit classification, demonstrating that the framework has the potential to effectively exploit the structure of unlabeled data to improve classification accuracy.

Zho (2005) has been introduced an EM algorithm for semi-supervised classification of a hyperspectral image. This method is iterative. In each step of algorithm, a label has been estimated for pixels without label and the classification has been finished after reaching the stop condition.

Lak et al. (2013) has been improved the previous algorithm. In cases that the training samples are not good enough (are few) for a class, a method has been presented for improving the covariance matrix estimation. The outliers have been detected by using of the local information of first and second neighbor pixels.

Table 1: Accuracy for each class and total accuracy

Also, a method has been proposed to determine the number of mixtures of each class.

In this work, the results are improved for outlier samples estimation. In this method the local information of the pixels of neighbors is used more than previous method. In the proposed method, the third neighbor is used too. and the outlier samples are detected by it. The results show the smaller number of iteration of EM algorithm and the time of running is reduced to half of previous model.

CONCLUSION

In this study a method has been proposed to detect outlier samples in each step of algorithm and remove them to estimate parameters in next stage. The proposed method by using more information of neighbor pixels of training samples leads to reduce the number of repetition of EM algorithm and in fact by this method the time of reach to the desirable accuracy and validation has been reduced in image classification. Figure 1 and 2 is shown the comparison between proposed algorithm and other methods in outlier sample detection for class 4. In this figures, the red color pixels shows the detected samples.

By implementing of this method, accuracy and validation of classification are 73.5 and 74.2, respectively. The number of repetition of algorithm has been reduced from 11-9 and the time of run reduced from 1370-560 sec. The variation of accuracy of image classes is shown in Table 1.

REFERENCES
1:  Rosset, S., J. Zhu, H. Zou and T. Hastie, 2008. A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning. In: Advances in Neural Information Processing Systems, Saul, L.K., Y. Weiss and L. Bottou (Eds.)., Chapter 17, MIT Press, Cambridge, MA., pp: 1161-1168.

2:  Von Luxburg, U., O. Bousquet and M. Belkin, 2005. Limits of Spectral Clustering. In: Advances in Neural Information Processing Systems 17, Saul, L.K., Y. Weiss and L. Bottou (Eds.). MIT Press, Cambridge, MA., pp: 857-864.

3:  Acuna, E. and C.A. Rodriguez, 2004. A meta analysis study of outlier detection methods in classification. Technical Paper, Department of Mathematics, University of Puerto Rico at Mayaguez.

4:  Becker, C. and U. Gather, 2001. The largest nonidentifiable outlier: A comparison of multivariate simultaneous outlier identification rules. Comput. Stat. Data Anal., 36: 119-127.
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5:  Becker, C. and U. Gather, 1999. The masking breakdown point of multivariate outlier identification rules. J. Am. Stat. Assoc., 94: 947-955.
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6:  Ratsaby, J. and S. Venkatesh, 1995. Learning from a mixture of labeled and unlabeled examples with parametric side information. Proceedings of the 8th Annual Conference on Computational Learning Theory, July 5-8, 1995, Santa Cruz, CA., USA., pp: 412-417.

7:  Nigam, K. and R. Ghani, 2000. Analyzing the Effectiveness and Applicability of Co-Training. Ninth International Conference on Information and Knowledge Management, November 6-11, 2000, McLean, VA., USA., pp: 86-93.

8:  Castelli, V. and T. Cover, 1996. The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter. IEEE Trans. Inf. Theory, 42: 2102-2117.
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9:  Lak, M., A. Keshavarz and H. Pourghassem, 2013. Graph-Based Hyperspectral Image Classification Using Outliers Detection Based on Spatial Information and Estimating of the Number of GMM Mixtures. (IEEE) International Conference on Communication Systems and Network Technologies, April 6-8, 2013, Washington, DC., USA., pp: 196-200.

10:  Zho, X., 2005. Semi-supervised learning with graphs. Ph.D. Thesis, Language Technologies Institute , School of Computer Science, Camegie Mellon University, Pennsylvania.

11:  Zhu, X., Z. Ghahramani and J. Lafferty, 2003. Semi-supervised learning using gaussian fields and harmonic functions. Proceedings of the 20th International Conference on Machine Learning, August 21-24, 2003, Washington, DC., USA., pp: 912-219.

12:  Bay, S.D. and M. Schwabacher, 2003. Mining distance-based outliers in near linear time with randomization and a simple pruning rule. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, DC., USA., pp: 29-38.

13:  Breunig, M.M., H.P. Kriegel, R.T. Ng and J. Sander, 2000. LOF: Identifying density-based local outliers. Proceedings of the International Conference on Management of Data, May 15-18, 2000, Dallas, TX., USA., pp: 93-104.

14:  Demiriz, A., K.P. Bennett and M.J. Embrechts, 1999. Semi-supervised clustering using genetic algorithms. Proceedings of the Artificial Neural Networks in Engineering Conference, November 7-10, 1999, ASME Press, pp: 809-824.

15:  Wagstaff, K., C. Cardie, S. Rogers and S. Schrodl, 2001. Constrained K-means clustering with background knowledge. Proceedings of the 18th International Conference on Machine Learning, June 28-July 1, 2001, San Francisco, CA., USA., pp: 577-584.

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