exploiting click constraints and multiview features for image ranking
K.M.Gowrish Kumar,G.Nagappan
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:4 Issue:3 Year: 06 May,2016 Pages:1025-1030
Abstract
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 image. 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 keeping one function unchanged and linearizing the other
Kewords
Click, hypergraph, learning to rank
Reference
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