http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : J. Anita Christaline (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
J. Anita Christaline,R. Ramesh,D. Vaishali 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.1
Feature based image Steganalysis demands the best feature model for accurate steganalysis. The extracted feature model includes the components in DCT features of JPEG image. Existing research in this field show extraction of different types of image features that show slightly improved classification accuracies. Though few recent methods of image steganalysis involve extracting all possible features of the image, they suffer dimensionality problem. The dataset used in our research include raw images from the BOSS database. The original dimension of the feature set extracted has 8726 features from 2000 images. While a larger feature set is expected to have all important information about the steganographic changes, it affects the classifier accuracy due to redundancy. To overcome the curse of dimensionality, we intend to introduce an unsupervised optimization technique before classification. The individual classifiers implemented are SVM and MLP and the fusion techniques implemented to combine these classifiers are Bayes, Dempster Schafer and Decision Template schemes. The performances of classifiers are analyzed for optimization based on Euclidean distance measure and Mahalanobis distance measure. Comparing individual classifiers, it has been found that SVM classifier outperforms MLP classifier for both Euclidean distance measure and Mahalanobis distance measure. Among the fusion schemes, the accuracy of Bayes fusion scheme proves to be best compared to Decision template and Dempster Schafer schemes. Also, the best possible classification accuracy has been obtained for Euclidean distance based optimization followed by Bayes fusion classifier scheme. The classification accuracies obtained in our research are better compared to existing methods.
-
D. Vaishali,R. Ramesh,J. Anita Christaline 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.6
Until the recent past, cancer diagnosis was made using histopathology methods, where the pathologists study biopsy samples and make inferences. These inferences are based on cell morphology and tissue distribution which represent randomness in growth and/or in placement. These methods are highly subjective/arbitrary and can sometimes lead to incorrect diagnosis. Nowadays, computer-assisted diagnostic (CAD), based on very large database, can aid in objective judgment. This study emphasizes the contribution of a two-dimensional (2D) autoregressive (AR) model for analysis and classification of histopathological images. In AR model, the parameters consist of a feature set of histopathological images obtained from biopsy samples taken from patients. These features are further used for analysis, synthesis and classification of cancer cells. The Yule-Walker Least Square (LS) method has been used for parameter estimation. The test statistics for the choice of a model order has also been suggested in this paper. It has been inferred that for a given sample image, the neighborhood is unique and solely depends on the properties of samples under consideration. Based on the features of AR parameters, samples are classified into two – healthy tissue and malignant tissue. The feature data sets have been classified using the linear kernel Support Vector Machine (SVM) classifier. In this work, we focus on measuring the performance of cancer diagnostic tests in terms of their recall, specificity, precision and F score. We observe that the fourth-order AR model gives promising results in performance evaluation using SVM classifier.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
