Computer Science ›› 2015, Vol. 42 ›› Issue (12): 275-277.

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Learning to Rank Based Approach for Image Searching

TAN Guang-xing and LIU Zhen-hui   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Image searching is one of the most important researches in image sharing based social networks.Traditional image searching methods usually compare the user keywords and the textual description of images in database while searching.Because the textual description is ambiguous,the abstracting of text for images is very hard,and thus the accuracy of image searching is low.In order to improve the accuracy of image searching,this paper proposed a learning to rank based approach.We described each image as a combination of multiple feature descriptors,and compared the similarity of the query and the image in database while users input a query of image.We applied association rules and support vector machine to learn the weight of each feature descriptor,and proposed corresponding learning algorithms.The experiments show that the proposed image searching approach is more accurate than related works while retrieving image for users.In addition,while using association rule and support vector machine to learn the classification functions,the two algorithms use the same instances to learn the weight of each feature descriptor,so they are relevant.

Key words: Image searching,Learning to rank,Support vector machine,Association rule

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