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An Attribute-assisted Reranking Model for Web Image Search

Image search reranking is an effective approach to re?ne the text-based image search result. Most existing reranking approaches are based on low-level visual features

An Attribute-assisted Reranking Model for Web Image Search


Image search Reranking is an effective approach to refine the text-based image search result. Most existing Reranking approaches are based on low-level visual features. exploit semantic attributes for image search Reranking. Based on the classifiers for all the predefined attributes, each image is represented by an attribute feature consisting of the responses from these classifiers. A hypergraph is then used to model the relationship between images by integrating low-level visual features and attribute features. Hypergraph ranking is then performed to order the images. Its basic principle is that visually similar images should have similar ranking scores. In this work, we propose a visual-attribute joint hypergraph learning approach to simultaneously explore two information sources.


Proposed to refine text-based search results by exploiting the visual information contained in the images.Graph based methods have been proposed recently and received increasing attention as demonstrated to be effective. The multimedia entities in top ranks and their visual relationship can be represented as a collection of nodes and edges.After a query “baby” is submitted, an initial result is obtained via a text-based search engine. It is observed that text-based search often returns “inconsistent” results.The experimental results demonstrate superiority of the proposed attribute-assisted reranking approach over other state-of-the-art reranking methods and their attribute-assisted variants.Then the re-ranked result list is created first by ordering the clusters according to the cluster conditional probability and next by ordering the samples within a cluster based on their cluster membership value. In a fast and accurate scheme is proposed for grouping Web image search results into semantic clusters. It is obvious that the clustering based reranking methods can work well when the initial search results contain many near duplicate media documents.

Proposed a semi-supervised framework to refine the text based image retrieval results via leveraging the data distribution and the partial supervision information obtained from the top ranked images.

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