In this paper we propose an approach to automatically annotate query images with keywords. We use kernel Canonical Correlation Analysis to learn a semantic representation between images and their associated documents. The semantic space provides a common representation and enables a comparison between the documents and images. This representation is then used in the creation of new document, co...

The restoration of a blurry or noisy image is commonly performed with a MAP estimator, which maximizes a posterior probability to reconstruct a clean image from a degraded image. A MAP estimator, when used with a sparse gradient image prior, reconstructs piecewise smooth images and typically removes textures that are important for visual realism. The three public datasets that were used in the ...

Visualization is a promising technique for both enhancing users’ perception of structure in the Internet and providing navigation facilities for its large information spaces. This paper describes an application of the Document Explorer to the visualization of WWW content structure. The system provides visualization, browsing, and query formulation mechanisms based on documents’ semantic content...

Visualization is a promising technique for both enhancing users’ perception of structure in the Internet and providing navigation facilities for its large information spaces. This paper describes an application of the Document Explorer to the visualization of WWW content structure. The system provides visualization, browsing, and query formulation mechanisms based on documents’ semantic content...

ferritin is a macromolecule and is responsible for the long term iron storage function in human serum and plasma. recent studies have highlighted the role of cell phone exposure on central nervous system, immune function and reproduction. the aim of this study was to investigate whether the human serum ferritin level could be interfered by the exposure to the 900 mhz gsm cell phones. fifty huma...

In this lecture, we examine the impact of the length of a document on its relevance to queries. We show that document relevance is positively correlated with document length, and see that relevance scores that use the normalization techniques we’ve studied so far (L∞, L1, L2) do not capture this correlation correctly. Finally, we present the “pivoted document length normalization” technique int...

This thesis report introduces a new technique of document clustering based on frequent senses. The developed system, named GDClust (Graph-Based Document Clustering) [1], works with frequent senses rather than dealing with frequent keywords used in traditional text mining techniques. GDClust presents text documents as hierarchical document-graphs and uses an Apriori paradigm to find the frequent...

Most of the existing document and web search engines rely on keyword-based queries. To find matches, these queries are processed using retrieval algorithms that rely on word frequencies, topic recentness, document authority, and (in some cases) available ontologies. In this paper, we propose an innovative approach to exploring text collections using a novel keywords-by-concepts (KbC) graph, whi...

Many researchers proposed different methodologies for the text skew estimation in binary images/gray scale images. They have been used widely for the skew identification of the printed text. There exist so many ways algorithms for detecting and correcting a slant or skew in a given document or image. Some of them provide better accuracy but are slow in speed, others have angle limitation drawba...

Word clouds have become one of the most widely accepted visual resources for document analysis and visualization, motivating the development of several methods for building layouts of keywords extracted from textual data. Existing methods are effective to demonstrate content, but are not capable of preserving semantic relationships among keywords while still linking the word cloud to the underl...