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Balakumar, N.
- An Ingenious Texture and Shape Feature Extraction in Remote Sensing Images by Means of Multi Kernel Principal Component analysis with Pyramidal Wavelet Transform and Canny Edge Detection Method
Abstract Views :184 |
PDF Views:3
Authors
N. Balakumar
1,
K. Ragul
1
Affiliations
1 Department of Computer Application, Pioneer College of Arts and Science, IN
1 Department of Computer Application, Pioneer College of Arts and Science, IN
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 3 (2018), Pagination: 1681-1685Abstract
In the rapid growth of the digital world, the dealing of remote sensing image is increased day to day in context with the extraction of information. The feature extractions had been an exigent part among the research to classify the remote sensing images for legitimate information reclamation. In such context this paper focus on the extraction of information from remote sensing images by means of classification of spectral classes. Texture and shape is one of the important features in computer vision for many applications. Most of the attention has been focused on texture features with window selection and noise models. This problem can be overcome through Multi Kernel Principal Component analysis with pyramidal wavelet transform and canny edge detection method for extracting feature in high resolute images based on texture and shape. In this paper, proposed Multi Kernel Principal Component analysis utilizes to extract common information and specify common sets of features for further process and reduces dimensionality. Pyramidal wavelet transform is used to extract texture perception for visual interpretation and it decomposes the images into number of descriptors. So texture can be extracted in an image with tree-structured wavelet. Finally, an edge detection technique identifies the boundary regions from the classified remote sensing image, which is taken as shape feature extraction. The performance of this proposed work is measured through peak signal to noise ratio, Execution time, Kappa analysis and structural similarity for a various remote sensing dataset images.Keywords
Feature Extraction, Texture, Edge Detection, Remote Sensing Images.References
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- A Web Personalization based on the Sequential Pattern Mining for Improved Web Access
Abstract Views :229 |
PDF Views:0
Authors
A. Vaishnavi
1,
N. Balakumar
1
Affiliations
1 Department of Computer Applications, Pioneer College of Arts and Science, IN
1 Department of Computer Applications, Pioneer College of Arts and Science, IN
Source
ICTACT Journal on Soft Computing, Vol 9, No 3 (2019), Pagination: 1903-1908Abstract
The development of information technology, the web has created a big challenge for directing the client to the website pages according to their need. Web page personalization process user’s query and retrieve the search results that corresponds to their interest. Accordingly, the option is to capture the intuition of the client and provide them a list of recommendation. The tedious work is, to find the user’s intuition. The web master of an institution ought to utilize methods of web mining to fetch the user’s intuition. The web usage mining is one the technique to find the users intuition. Web usage mining can provide patterns of usage to the organizations in order to obtain user profiles and therefore they can make easier the website browsing or present specific pages. The recommendation is one of the applications in web usage mining. Recommender systems area unit one of the most common and easily apprehensible applications. There square measure 2 major ways in which most of advice engines work. They can either rely on the properties of the things that every user likes, discovering what else the user might like. In this paper, we tend to propose a recommendation approach that recommends a number of web pages based on user’s interest upon client’s history, from the web log. In this approach, it brings the most accuracy of the web pages to be displayed for the user.Keywords
Web Usage Mining, Recommendation, Web Personalization, Web Log, Sequential Pattern Mining, Web Mining.References
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