Informatics Publishing Limited

B. Raviteja ¹, M. Surendra Prasad Babu ², K. Venkata Rao ², J. Harikiran ¹

Affiliations
1 Department of Information Technology, Gandhi Institute of Technology and Management, Beach Road,Gandhi Nagar, Rushikonda, Visakhapatnam – 530045, Andhra Pradesh, IN
2 Department of Computer Science and Systems Engineering, Andhra University, Waltair Junction,Visakhapatnam – 530003, Andhra Pradesh, IN

Abstract

Hyperspectral imaging system contains stack of images collected from the sensor with different wavelengths representing the same scene on the earth. This paper presents a framework for hyperspectral image segmentation using a clustering algorithm. The framework consists of four stages in segmenting a hyperspectral data set. In the first stage, filtering is done to remove noise in image bands. Second stage consists of dimensionality reduction algorithms, in which the bands that convey less information or redundant data will be removed. This deletion will decrease the storage requirement, computational load etc in processing the hyperspectral data. In the third stage, the informative bands which are selected in the second stage are merged into a single image using hierarchical fusion technique. The main goal of image fusion is to combine all the information from the selected image bands to form a single image. This single image is segmented using Fuzzy C-means (FCM) algorithm. The qualitative and quantitative analysis shows that this framework will segment the data set more accurately by combining all the features in the image bands.

Keywords

Dimensionality Reduction, Empirical Mode Decomposition, FCM, Hyperspectral Imaging.

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R. Chandramma ¹, R. C. Ravindranath ¹, B. Raviteja , Y. B. Ravikumar , S. Venkatesh , M. Yashwanth

Affiliations
1 Department of Computer Science and Engineering, Vivekananda Institute of Technology, Bangalore-74, IN

Abstract

The rapid development of the internet and web publishing techniques create numerous information sources published as HTML pages on World Wide Web (WWW). However, there is lot of redundant and irrelevant information also on web pages like Navigation panels, Table of content (TOC), advertisements, copyright statements etc. There are various technologies & researches which are focusing on the extraction of relevant information from large web data storage. But still there is requirement of availability of automatic annotation of this extracted information into a systematic way so to be processed further for various purposes and for language independent also. In this system we present a simple, robust, accurate and language-independent solution for extracting the main content of an HTML formatted Web page and for removing additional content such as navigation menus, functional and design elements, and commercial advertisements. Accurate and efficient content extraction from Web pages is largely needed when searching or mining Web content. So in this system we use a new approach for content extraction called word to leaf ratio and density of links.

Keywords

Content Extraction, Entropy, Document Object Model, Template, Content Structure Tree, Web Page Segmentation, Clustering, Anchor Text.

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