The inconsistency between textual features and visual contents can cause poor image search results. To solve this problem, click features, which are more reliable than textual information in justifying the relevance between a query and clicked images, are adopted in image ranking model. The learning to rank approach has also been widely used in image retrieval. The query dependent features for each image are extracted from textual information to describe the relationship between a query and an

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... mage. The existing ranking model cannot integrate visual features, which are efficient in refining the click-based search results, a novel ranking model based on the learning to rank frame work. Visual features and click features are simultaneously utilized to obtain the ranking model. This algorithm alternately minimizes two different approximations of the original objective function by function. Keywords-Click, hyper graph, learning to rank. I INTRODUCTION Data mining is the process of analyzing data from different perspectives and summarizing it into useful information-information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Ranking has recently come to be regarded as a learning problem and some machine learning algorithm have been applied to it. To measure the performance of a search engine,. The learning to rank approach has also been widely used in image retrieval. The query dependent features for each image are extracted from textual information to describe the relationship between a query and an image. The textual information sources include the title, the surrounding text, the HTML alternative texts, or the titles of the host webs. The query related features can be extracted to represent the relationship between the query and the visual contents, and the textual features can then be integrated with them. The learning to rank approach has also been widely used in image retrieval. The query dependent features for each image are extracted from textual information to describe the relationship between a query and an image. nagappan.cse@saveetha.ac.in The aim of our project for consistency is to define ranking based image retrieve in using Admin click. In our project re-ranking method using Admin based click image retrieve. Learning to rank model called VCLTR which jointly considers visual features and click features in image retrieval. A robust and accurate ranking model can be built by using the click features, and the visual features are effective in further enhancing the model's performance. Learning to rank model called VCLTR which jointly considers visual features and click features in image retrieval. A robust and accurate ranking model can be built by using the click features, and the visual features are effective in further enhancing the model's performance. II REQUIREMENTS The consistency is to define ranking based image retrieve in using Admin click. In our project re-ranking method using Admin based click image retrieve. Learning to rank model called VCLTR which jointly considers visual features and click features in image retrieval. A robust and accurate ranking model can be built by using the click features, and the visual features are effective in further enhancing the model's performance • The learning to rank approach has also been widely used in image retrieval. • In general, given a query, the learning to rank system retrieves data from the collection and returns the top-ranked data.