Attribute Assisted Re-Ranking Model based on Web Image Search

Computer Engineering

Images search engines such as Google Browser have relied on matching textual information of the images against queries given by users. However, text-based image retrieval suffers from essential problem that are caused mainly by the incapability of the associated text to appropriately describe the image content. Today we can see that the existing visual reranking methods can be typically categorized into three categories as the clustering based, classification based and graph based methods. The clustering based reranking methods stem from the key observation that a wealth of visual characteristics can be shared by relevant images. In the schema based methods, visual reranking is formulated as binary Technique problem aiming to identify whether each search result is relevant or not. 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.

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