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Learning to Rank Using User Clicks and Visual Features for Image Retrieval

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. However, the existing ranking model cannot integrate visual features, which are efficient in refining the click-based search results. In this paper, we propose a novel ranking model based on the learning to rank framework. Visual features and click features are simultaneously utilized to obtain the ranking model. 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 regularize term. In accordance with the fast alternating linearization method, we design a novel algorithm to optimize the objective function. This algorithm alternately minimizes two different approximations of the original objective function by keeping one function unchanged and linear zing the other.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:- In existing process many existing re-ranking methods are based on implicitly adopting pseudo-relevance feedback (PRF). Visual re-ranking methods cannot successfully relegate irrelevant images which have originally been allocated a high rank, and suffer from an unreliable original ranking list because the textual information cannot accurately describe the semantics of the queries.	PROPOSED CONCEPT: - Proposed a classification-based method which utilizes uppermost images as pseudo-positive and undermost images as pseudo-negative examples to train a classifier and conduct re-ranking. The query dependent features for each image are extracted from textual information to describe the relationship between a query and an image.
EXISTING TECHNIQUE:- Visual Re-ranking.	PROPOSED TECHNIQUE:- Visual and click features based learning to rank (VCLTR).
TECHNIQUE DEFINITION:- Combining visual information is visual re-ranking. This combines both the textual and visual information and returns visually satisfying retrieved results. The ranking list of images obtained from the text-based search can be regarded as a reasonable baseline with certain noises. The visual information of the images is then adopted to shift the related images to the top of the ranking list.	TECHNIQUE DEFINITION:- Using the click features creates a robust and accurate ranking model, and adopting the visual features will further enhance the model’s performance. VCLTR is evaluated over a large-scale and practical image search dataset, in which the click features are collected from real web users. The experimental results suggest the effectiveness of our method.
DRAWBACKS:- Existing commercial image search engines usually suffer from imperfect results caused by the noisy textual description in visual search. In this model cannot integrate visual features, which are efficient in refining the click-based search results.	ADVANTAGES:- Visual and click information are simultaneously utilized in the learning process for ranking. Accurate ranking model can be learned from this framework because the noises in click features will be removed by the visual content.