Abstract— Images are the increase day by day on the Internet, retrieving relevant images from a large collection of database images has become an important research topic. This paper focus on the reranking of images by utilizing the both the visual and textual features. So given a textual query in traditional image retrieval, relevant images are to be re-ranked using visual features after the initial text-based search. Here first query keywords are utilize for separating the dataset images into two group of relevant image and irrelevant image then all the images are ranked base on the image different modality of image features as the similar images need to be display closer. Using single modality is not effective as different image need different kind of feature for analysis and it was obtained in experimental that the proposed re-ranking approach has better performanceis than using single modality.
There are 7 global features extracted, including
Conclusions: World Wide Web has necessitated the users to make use of automated tools to locate desired information resources and to follow. Web image re-ranking has been widely used to reduce the user searching time on the internet; its success mainly depend on the accuracy of image features similarities. This paper present utilizing of the new text as well as visual features for ranking the image as both make the re-ranking process more powerful, which is shown in results.
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