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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Defa Hu
Hunan University of Commerce
Computer Science & IT

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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

 1 
RVM Classification of Hyperspectral Images Based on Wavelet Kernel Non-negative Matrix Fractorization Lin Bai; Defa Hu; Meng Hui; Yanbo Li
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 3: September 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i3.1805

Abstract

A novel kernel framework for hyperspectral image classification based on relevance vector machine (RVM) is presented in this paper. The new feature extraction algorithm based on Mexican hat wavelet kernel non-negative matrix factorization (WKNMF) for hyperspectral remote sensing images is proposed. By using the feature of multi-resolution analysis, the new method of nonlinear mapping capability based on kernel NMF can be improved. The new classification framework of hyperspectral image data combined with the novel WKNMF and RVM. The simulation experimental results on HYDICE and AVIRIS data sets are both show that the classification accuracy of proposed method compared with other experiment methods even can be improved over 10% in some cases and the classification precision of small sample data area can be improved effectively.
A Neighbor-finding Algorithm Involving the Application of SNAM in Binary-image Representation Jie He; Hui Guo; Defa Hu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 4: December 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i4.1899

Abstract

In view of the low execution efficiency and poor practicability of the existing neighbor-finding method, a fast neighbor-finding algorithm is put forward on the basis of Square Non-symmetry and Anti-packing Model (SNAM) for binary-image. First of all, the improved minor-diagonal scanning way is applied to strengthen SNAM’s adaptability to various textures, thus reducing the total number of nodes after coding; then the storage structures for its sub-patterns are standardized and a grid array is used to recover the spatial-position relationships among sub-patterns, so as to further reduce the complexity of the neighbor-finding algorithm. Experimental result shows that this method’s execution efficiency is significantly higher than that of the classic Linear Quad Tree (LQT)-based neighbor-finding method.
Applications of Improved Ant Colony Optimization Clustering Algorithm in Image Segmentation Junhui Zhou; Defa Hu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 3: September 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i3.1803

Abstract

When expressing the data feature extraction of the interesting objectives, image segmentation is to transform the data set of the features of the original image into more tight and general data set. This paper explores the image segmentation technology based on ant colony optimization clustering algorithm and proposes an improved ant colony clustering algorithm (ACCA). It improves and analyzes the computational formula of the similarity function and improves parameter selection and setting by setting ant clustering rules. Through this algorithm, it can not only accelerate the clustering speed, but it can also have a better clustering partitioning result. The experimental result shows that the method of this paper is better than the original OTSU image segmentation method in accuracy, rapidity and stability.
A Sparse Representation Image Denoising Method Based on Orthogonal Matching Pursuit Xiaojun Yu; Defa Hu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 4: December 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i4.1901

Abstract

Image denoising is an important research aspect in the field of digital image processing, and sparse representation theory is also one of the research focuses in recent years. The sparse representation of the image can better extract the nature of the image, and use a way as concise as possible to express the image. In image denoising based on sparse representation, the useful information of the image possess certain structural features, which match the atom structure. However, noise does not possess such property, therefore, sparse representation can effectively separate the useful information from noise to achieve the purpose of denoising. Aiming at image denoising problem of low signal-to-noise ratio (SNR) image, combined with Orthogonal Matching Pursuit and sparse representation theory, this paper puts forward an image denoising method. The experiment shows that compared with the traditional image denoising based on Symlets, image denoising based on Contourlet transform, this method can delete noise in low SNR image and keep the useful information in the original image more efficiently.
Co-Authors Hui Guo Jie He Junhui Zhou Lin Bai Meng Hui Xiaojun Yu Xiuhu Tan Yanbo Li