LEARNING TO RANK FOR WEB IMAGE RETRIEVAL BASED ON GENETIC PROGRAMMING
Ranking is a crucial task in information retrieval systems. This paper proposes a novel ranking model named WIRank, which employs a layered genetic programming architecture to automatically generate an effective ranking function, by combining various types of evidences in web image retrieval, including text information, image-based features and link structure analysis. This paper also introduces a new significant feature to represent images: Temporal Information, which is rarely utilized in the current information retrieval systems and applications. 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.
Web image retrieval ranking function genetic programming graph theory temporal information
Li Piji Ma Jun
School of Computer Science & Technology Shandong University, Jinan, 250101,China
国际会议
北京
英文
137-142
2009-10-18（万方平台首次上网日期，不代表论文的发表时间）