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Min-uncertainty & max-certainty criteria of neighborhood rough-mutual feature selection
(2017-01-01)
Foithong, Sombut
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Srinil, Phaitoon
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Pinngern, Ouen
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Attachoo, Boonwat
Feature Selection (FS) is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Most existing FS methods based on rough set theory use the dependency function for evaluating the goodness of a feature subset. However, these FS methods may unsuccessfully be applied on dataset with noise, which determine only information from a positive region but neglect a boundary region. This paper proposes a criterion of the maximal lower approximation information (Max-Certainty) and minimal boundary region information (Min-Uncertainty), based on neighborhood rough set and mutual information for evaluating the goodness of a feature subset. We combine this proposed criterion with neighborhood rough set, which is directly applicable to numerical and heterogeneous features, without involving a discretization of numerical features. Comparing it with the rough set based approaches, our proposed method improves accuracy over various experimental data sets. Experimental results illustrate that much valuable information can be extracted by using this idea. This proposed technique is demonstrated on discrete, continuous, and heterogeneous data, and is compared with other FS methods in terms of subset size and classification accuracy.
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Optimum watermark detection of ultrasonic echo medical images
(2015-03-01)
Khawne, Amnach
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Attachoo, Boonwat
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Hamamoto, Kazuhiko
To prevent unauthorized access to a person's medical images, it is widely acknowledged that the security features of confidentiality, availability and integrity should be in place. This paper proposes the watermarking of ultrasonic echo images together with optimal watermark detection, in which pseudorandom noise is added to the images for integrity. The optimum watermark detection is the integration of the generalized Gaussian distribution (ρ-GGD) and the Cauchy distribution. The results show that the proposed method gives good detection performance. The proposed method not merely achieves optimum detection using the Rao test but also leads to the highest detection probability with JPEG2000 compression. Compared with other detection methods, our proposed method exhibits better watermark detection performance even when the watermark-to-document ratio (WDR) is -50 dB.
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Content-based image retrieval system based on combined and weighted multi-features
(2013-12-31)
Bounthanh, Machine
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Attachoo, Boonwat
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Hamamoto, Kazuhiko
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Bounthanh, Tha
This paper, we proposed a novel framework for combining and weighting all of three i.e. color, shape and texture features to achieve higher retrieval efficiency. The color feature is extracted by quantifying the YUV color space and the color attributes like the mean value, the standard deviation, and the image bitmap of YUV color space is represented. The texture features are obtained by the entropy based on the gray level cooccurrence matrix and the edge histogram descriptor of an image. The shape feature descriptor is derived from Fourier descriptors (FDs) and the FDs derived from different signatures. When computing the similarity between the query image and target image in the database, normalization information distance is also used for adjusting distance values into the same level. And then the linear combination has used to combine the normalized distance of the color, shape and texture features to obtain the similarity as the indexing of image. Furthermore, an experimental results indicated, a weight variation to achieve higher retrieval efficiency and the proposed technique indeed outperforms other schemes in terms of the accuracy and efficiency. © 2013 IEEE.
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Feature subset selection wrapper based on mutual information and rough sets
(2012-01-01)
Foithong, Sombut
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Pinngern, Ouen
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Attachoo, Boonwat
In this paper, we introduced a novel feature selection method based on the hybrid model (filter-wrapper). We developed a feature selection method using the mutual information criterion without requiring a user-defined parameter for the selection of the candidate feature set. Subsequently, to reduce the computational cost and avoid encountering to local maxima of wrapper search, a wrapper approach searches in the space of a superreduct which is selected from the candidate feature set. Finally, the wrapper approach determines to select a proper feature set which better suits the learning algorithm. The efficiency and effectiveness of our technique is demonstrated through extensive comparison with other representative methods. Our approach shows an excellent performance, not only high classification accuracy, but also with respect to the number of features selected. © 2011 Elsevier Ltd. All rights reserved.
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Rough-mutual feature selection based on min-uncertainty and max-certainty
(2012-01-01)
Foitong, Sombut
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Pinngern, Ouen
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Attachoo, Boonwat
Feature selection (FS) plays an important role in pattern recognition and machine learning. FS is applied to dimensionality reduction and its purpose is to select a subset of the original features of a data set which is rich in the most useful information. Most existing FS methods based on rough set theory focus on dependency function, which is based on lower approximation as for evaluating the goodness of a feature subset. However, by determining only information from a positive region but neglecting a boundary region, most relevant information could be invisible. This paper, the maximal lower approximation (Max Certainty) minimal boundary region (Mm Uncertainty) criterion, focuses on feature selection methods based on rough set and mutual infonnation which use different values among the lower approximation information and the information contained in the boundary region. The use of this idea can result in higher predictive accuracy than those obtained using the measure based on the positive region (certainty region) alone. This demonstrates that much valuable information can be extracted by using this idea. Experimental results are illustrated for discrete, continuous, and microarray data and compared with other FS methods in terms of subset size and classification accuracy. key words: rough sets, mutual information, feature selection, boundary region, classification accuracy. Copyright © 2012 The Institute of Electronics, Information and Communication Engineers.
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Linear filtering for optimized approach in satellite image enhancement
(2010-11-11)
Pattanasethanon, Petcharat
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Attachoo, Boonwat
Problem statement: For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied. Approach: In this study, we proposed a new method to enhance the satellite image which compares two procedures using two different kinds of filtering technique with an additional step in order to obtain the perceived image. In this new algorithm we first transform the color image into grayscale. The image is then preceded to the edge detection and brightness enhancement step using Laplacian and Sobel technique individually. Results: From the results, the Tenengrad averred that the enhancement result of the dimension and depth in the image were successfully classified. We also evaluate the image quality, adjusting by the PSNR and Tenengrad criterion which indicates that the proposed method shows dramatically increase in pixel distribution throughout the range of RGB. Conclusion: The result of this research is also beneficial in terms of geographical views due to the process which determined the difference appeared on each area. Eventually, this research also performed a comparison for the enhancement step mentioned in this study. © 2010 Science Publications.
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A unified histogram and laplacian based for image sharpening
(2009-12-01)
Pattanasethanon, Petcharat
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Attachoo, Boonwat
An effective method with enhancement procedures is proposed for image sharpening. Histogram equalization and edge detecting procedures are applied to original images. The mean value, standard deviation, and signal to noise ratio are defined as the statistical index which specifies the brightness, resolution, as well as the sharpness of the image. From the result of output images, the brightness and the contrast of the images were enhanced simultaneously with the sharpness and clearness of the mesh which outweigh the original image appearance. In addition, the advantages of this research indicate a suitable sharpening technique for the image category. ©2009 IEEE.
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Growing rule-based induction system
(2009-11-12)
Rojanavasu, Pornthep
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Attachoo, Boonwat
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Pinngern, Ouen
Learning Classifier Systems (LCSs) are rule-based systems that have widely been used in data mining over the last few years. This paper employs UCS, a supervised learning classifier system, that was a version of LCSs for classification in data mining tasks. In this paper, we propose an adaptive framework of a rule-based competitive learning environment. In this framework, a growing neural gas (GNG) is used to adaptively cluster the data instances as they arrive. Each instance is then assigned to based classifier, the UCS responsible for the corresponding cluster. Through this mechanism, the complexity of a classification problem is decomposed adaptively into subproblems, each with a lower or equal complexity to the overall problem. Since each instance is exposed to a smaller population size than the single population approach, the throughput of the system increases. The experiments show that the proposed framework can decompose a problem adaptively into several subproblems. The accuracy rate of UCS in the distributed environment can also be better than the normal environment. © 2009 IEEE.
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Estimating optimal feature subsets using mutual information feature selector and rough sets
(2009-01-01)
Foitong, Sombut
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Rojanavasu, Pornthep
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Attachoo, Boonwat
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Pinngern, Ouen
Mutual Information (MI) is a good selector of relevance between input and output feature and have been used as a measure for ranking features in several feature selection methods. Theses methods cannot estimate optimal feature subsets by themselves, but depend on user defined performance. In this paper, we propose estimation of optimal feature subsets by using rough sets to determine candidate feature subset which receives from MI feature selector. The experiment shows that we can correct nonlinear problems and problems in situation of two or more combined features are dominant features, maintain an improve classification accuracy. © Springer-Verlag Berlin Heidelberg 2009.
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A new approach for colored satellite image enhancement
(2008-01-01)
Attachoo, Boonwat
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Pattanasethanon, Petcharat
A unique method of image filtering has been developed that enhances the detail and sharpens the edges of colored satellite images. Histogram equalization coupled with a two stage data filtering process that applies convolution with laplacian and sharpening with laplacaian through the 3 color bands that produce the colored satellite images has yielded sharper clearer images. The initial enhancement using histogram equalization was followed by the first stage of a filtering process convolution with laplacian which highlighted the edges of the image. The application of the second stage filtering sharpening with laplacian yielded enhanced color reproduction and a more accurate depiction of information at sea and land levels than was available in the original image. An analysis of the statistical index and signal to noise ratio of the true color and false color of histogram equalization, convolution with laplacian and sharpening images showed the image. An analysis of the false color of histogram equalization, convolution with laplacian and sharpening images showed the image to be superior, in this study the multi spectral content in the satellite image was transformed into a composite colour image, and then convoluted using the laplacian technique. This study placed an emphasis on improving the detail and edge clarity of satellite images. © 2008 IEEE.

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