A Relevant Score Normalization Method Using Shannon’s Information Measure

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• 作者：Yu Suzuki ; Kenji Hatano ; Masatoshi Yoshikawa ; Shunsuke Uemura ; Kyoji Kawagoe
• 刊名：Lecture Notes in Computer Science
• 出版年：2005
• 出版时间：2005
• 年：2005
• 卷：3815
• 期：1
• 页码：pp.311-316
• 全文大小：314 KB

文摘

Given the ranked lists of images with relevance scores returned by multiple image retrieval subsystems in response to a given query, the problem of combined retrieval system is how to combine these lists equivalently. In this paper, we propose a novel relevance score normalization method based on Shannon’s information measure. Generally, the number of relevant images is exceedingly smaller than that of the entire retrieval targets. Therefore, we suppose that if the subsystems can clearly identify which retrieval targets are relevant, the subsystems should calculate high relevance scores to a few retrieval targets. In short, we can calculate the sureness of the IR subsystem using the distribution of the relevance scores. Then, we calculate the sureness of the IR subsystems using Shannon’s information measure, and calculate the normalized relevance scores using the sureness of the IR subsystems and the raw relevant scores. In our experiment, our normalization method outperformed the others.