利用遗传编程排序图像 |
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基金项目: | supported by the Natural Science Foundation of China (60970047); the Natural Science Foundation of Shandong Province (Y2008G19); the Key Science-Technology Project of Shandong Province (2007GG10001002, 2008GG10001026) |
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摘 要: | Web image retrieval is a challenging task. One central problem of web image retrieval is to rank a set of images according to how well they meet the user information need. The problem of learning to rank has inspired numerous approaches to resolve it in the text information retrieval, related work for web image retrieval, however, are still limited. We focus on the problem of learning to rank images for web image retrieval, and propose a novel ranking model, which employs a genetic programming architecture to automatically generate an effective ranking function, by combining various types of evidences in web image retrieval, including text information, image visual content features, link structure analysis and temporal information. The experimental results show that the proposed algorithms are capable of learning effective ranking functions for web image retrieval. Significant improvement in relevancy obtained, in comparison to some other well-known ranking techniques, in terms of MAP, NDCG@n and D@n.
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关 键 词: | web image retrieval learning to ranking temporal information genetic programming results diversity |
收稿时间: | 2011-07-15; |
Use Genetic Programming to Rank Web Images |
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Authors: | Li Piji Ma Jun School of Computer Science & Technology Sh ong University Jinan China |
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Institution: | Li Piji,Ma Jun School of Computer Science & Technology,Sh,ong University,Jinan 250101,China |
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Abstract: | Web image retrieval is a challenging task. One central problem of web image retrieval is to rank a set of images according to how well they meet the user information need. The problem of learning to rank has inspired numerous approaches to resolve it in the text information retrieval, related work for web image retrieval, however, are still limited. We focus on the problem of learning to rank images for web image retrieval, and propose a novel ranking model, which employs a genetic programming architecture ... |
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Keywords: | web image retrieval learning to ranking temporal information genetic programming results diversity |
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