Dynamic Image Re ranking and unused Image Removal based on user click

Author(s):  W.Shibi getziyal, B.Bhuvaneswari,R.Latha

Abstract:   Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently and significant progress has been made. Which are more reliable than textual information in justifying the relevance between a query and clicked images,are adopted in image ranking model. However 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 framework, Visual Features and click features are simultaneously utilized to obtain the ranking model. Specifically, the proposed approach is based on large margin structured output learning and the visual consistency is integrated with the click features through a hyper graph regular term. In accordance with the fast alternating linearization method, we design a novel algorithm to optimize the objective function To improve the performance of keyword based image search engines by re-ranking their original results and we remove the unused images from the image search engines and unstructural result to structural result is done.