QDFA: Query-dependent feature aggregation for medical image retrieval

Yonggang Huang; Dianfu Ma; Jun Zhang; Yongwang Zhao

doi:10.1587/transinf.E95.D.275

QDFA: Query-dependent feature aggregation for medical image retrieval

Yonggang Huang^*, Dianfu Ma, Jun Zhang, Yongwang Zhao

^*此作品的通讯作者

科研成果: 期刊稿件 › 文章 › 同行评审

1 引用（Scopus）

摘要

We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods.

源语言	英语
页（从-至）	275-279
页数	5
期刊	IEICE Transactions on Information and Systems
卷	E-95-D
期	1
DOI	https://doi.org/10.1587/transinf.E95.D.275
出版状态	已出版 - 1月 2012
已对外发布	是

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10.1587/transinf.E95.D.275

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title = "QDFA: Query-dependent feature aggregation for medical image retrieval",

abstract = "We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods.",

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AU - Zhang, Jun

AU - Zhao, Yongwang

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AB - We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods.

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KW - Fuzzy SVM

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QDFA: Query-dependent feature aggregation for medical image retrieval

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