We participated in the ImageCLEF 2011 Photo Annotation and Wikipedia Image Retrieval Tasks. Our approach to the ImageCLEF 2011 Photo Annotation is based on a kernel weighting procedure using visual Fisher kernels and a Flickr-tag based JensenShannon divergence based kernel. We trained a Gaussian Mixture Model (GMM) to define a generative model over the feature vectors extracted from the image p...

Web 2.0 has an architecture for participation and sharing, thus encouraging users to add value to an application. This study proposes a refined Collaborative Content Sharing Module that better equips the LCMS to enhance the user experience on content sharing. Within this module, a concept space that brings both functions of tag recommendation and concept based search is generated using the alli...

Information mining is now emerging term in recent days. This helps the user to gain knowledge and learning new information as for their need image searching is also a type of information searching. The current computer vision techniques extract from images mostly low-level features and the link between low-level features and high-level semantics of image content is lost. Neither a single low-le...

Problem statement: To increase traffic on websites, Search Engine Optimization (SEO) has provided many costly and time-consuming options. One problem is the inadequate distribution of keywords especially those keywords that users use the title tag and Meta tags. Approach: This study described work on an initial model for handling some of the SEO factors to increase the distribution of keywords....

Content organization over the Internet went through several interesting phases of evolution: from structured directories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step towards the semantic web vision. Tagging allows ranking and data organization to directly utilize inputs from end users, enabling machine processing of Web content. S...

This work proposes an approach to collaborative tag recommendation based on a machine learning system for probabilistic regression. The goal of the method is to support users of current social network systems by providing a rank of new meaningful tags for a resource. This system provides a ranked tag set and it feeds on different posts depending on the resource for which the recommendation is r...

Learning To Rank (LTR) techniques aim to learn an effective document ranking function by combining several document features. While the function learned may be uniformly applied to all queries, many studies have shown that different ranking functions favour different queries, and the retrieval performance can be significantly enhanced if an appropriate ranking function is selected for each indi...